Read the Docs build information
Build id: 1403244
Project: datarobot-public-api-client
Version: early-access
Commit: 97790807cdf0159f7709b86e11e9798e8dc467b8
Date: 2023-04-10T16:46:46.571860Z
State: finished
Success: True


[rtd-command-info] start-time: 2023-04-10T16:46:48.388301Z, end-time: 2023-04-10T16:46:54.169652Z, duration: 5, exit-code: 0
git clone --no-single-branch --depth 50 git@github.com:datarobot/public_api_client.git .
Cloning into '.'...
Warning: Permanently added 'github.com,192.30.255.112' (ECDSA) to the list of known hosts.

[rtd-command-info] start-time: 2023-04-10T16:46:54.356029Z, end-time: 2023-04-10T16:46:54.705027Z, duration: 0, exit-code: 0
git checkout --force 97790807cdf0159f7709b86e11e9798e8dc467b8
Note: switching to '97790807cdf0159f7709b86e11e9798e8dc467b8'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 97790807 [DSX-2640] Mypy Final Pass Part 2 (#2295)

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git clean -d -f -f


[rtd-command-info] start-time: 2023-04-10T16:46:56.141610Z, end-time: 2023-04-10T16:47:00.793502Z, duration: 4, exit-code: 0
python3.7 -mvirtualenv $READTHEDOCS_VIRTUALENV_PATH
Using base prefix '/home/docs/.pyenv/versions/3.7.9'
New python executable in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/early-access/bin/python3.7
Also creating executable in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/early-access/bin/python
Installing setuptools, pip, wheel...
done.

[rtd-command-info] start-time: 2023-04-10T16:47:00.987016Z, end-time: 2023-04-10T16:47:02.836611Z, duration: 1, exit-code: 0
python -m pip install --upgrade --no-cache-dir pip setuptools<58.3.0
Requirement already satisfied: pip in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/early-access/lib/python3.7/site-packages (23.0.1)
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Installing collected packages: setuptools
  Attempting uninstall: setuptools
    Found existing installation: setuptools 67.6.1
    Uninstalling setuptools-67.6.1:
      Successfully uninstalled setuptools-67.6.1
Successfully installed setuptools-58.2.0

[rtd-command-info] start-time: 2023-04-10T16:47:03.021482Z, end-time: 2023-04-10T16:47:09.889924Z, duration: 6, exit-code: 0
python -m pip install --upgrade --no-cache-dir pillow==5.4.1 mock==1.0.1 alabaster>=0.7,<0.8,!=0.7.5 commonmark==0.9.1 recommonmark==0.5.0 sphinx<2 sphinx-rtd-theme<0.5 readthedocs-sphinx-ext<2.3 jinja2<3.1.0
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Building wheels for collected packages: mock
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Successfully built mock
Installing collected packages: snowballstemmer, pytz, mock, commonmark, urllib3, sphinxcontrib-serializinghtml, six, Pygments, pillow, packaging, MarkupSafe, imagesize, idna, docutils, charset-normalizer, certifi, babel, alabaster, sphinxcontrib-websupport, requests, jinja2, sphinx, readthedocs-sphinx-ext, sphinx-rtd-theme, recommonmark
Successfully installed MarkupSafe-2.1.2 Pygments-2.15.0 alabaster-0.7.13 babel-2.12.1 certifi-2022.12.7 charset-normalizer-3.1.0 commonmark-0.9.1 docutils-0.17.1 idna-3.4 imagesize-1.4.1 jinja2-3.0.3 mock-1.0.1 packaging-23.0 pillow-5.4.1 pytz-2023.3 readthedocs-sphinx-ext-2.2.0 recommonmark-0.5.0 requests-2.28.2 six-1.16.0 snowballstemmer-2.2.0 sphinx-1.8.6 sphinx-rtd-theme-0.4.3 sphinxcontrib-serializinghtml-1.1.5 sphinxcontrib-websupport-1.2.4 urllib3-1.26.15

[rtd-command-info] start-time: 2023-04-10T16:47:10.088716Z, end-time: 2023-04-10T16:47:52.787856Z, duration: 42, exit-code: 0
python -m pip install --upgrade --upgrade-strategy eager --no-cache-dir .[dev]
Processing /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/early-access
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[rtd-command-info] start-time: 2023-04-10T16:47:55.172019Z, end-time: 2023-04-10T16:47:55.278280Z, duration: 0, exit-code: 0
cat docs/conf.py
#
# Copyright 2021 DataRobot, Inc. and its affiliates.
#
# All rights reserved.
#
# DataRobot, Inc.
#
# This is proprietary source code of DataRobot, Inc. and its
# affiliates.
#
# Released under the terms of DataRobot Tool and Utility Agreement.
#
# DataRobot Python API documentation build configuration file, created by
# sphinx-quickstart on Mon Mar 16 10:50:19 2015.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

import os
import shutil
import sys
from zipfile import ZipFile

sys.path.insert(0, os.path.abspath(".."))
from datarobot import __version__ as version  # noqa

# Make sure magic 'tags' field is defined.
# (Not actually useful but makes Cython and pylint errors go away.)
if "tags" not in locals():
    tags = None
if "b" in version:
    # mainline quarterly releases never have the beta "b" in the version, only internal & weekly do
    tags.add("include_experimental_docs")

# Copy the changelog to the docs directory so sphinx will include it
root_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
changelog_path = os.path.join(root_dir, "CHANGES.rst")
dest_path = os.path.join(root_dir, "docs", "CHANGES.rst")
assert os.path.isfile(changelog_path)
shutil.copyfile(changelog_path, dest_path)

# Create a zip file of the example resources (csv, py, ipynb)
# so that it can be included it as a download within the HTML docs
files_to_zip = []
for root, _, files in os.walk("examples"):
    basename = os.path.basename(root)
    # skip "hidden" directories like notebook autosaves and _build
    if not basename.startswith("_") and not basename.startswith("."):
        for file in files:
            name, ext = os.path.splitext(file)
            ext_lower = ext.lower()
            if ext_lower in [".csv", ".xls", ".xlsx", ".ipynb", ".py"]:
                files_to_zip.append(os.path.join(root, file))
with ZipFile("datarobot_examples.zip", "w") as examples_zip:
    for file in files_to_zip:
        examples_zip.write(file)

# -- General configuration -----------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
# needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = [
    "sphinx.ext.autodoc",
    "sphinx.ext.autosummary",
    "sphinx.ext.doctest",
    "numpydoc",
    "nbsphinx",
]

# make warnings go away- as advised by
# https://github.com/phn/pytpm/issues/3#issuecomment-12133978
numpydoc_show_class_members = False

nbsphinx_execute = "never"

# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]

# The suffix of source filenames.
source_suffix = ".rst"

# Showing automatically documented members in same order as they are in source
autodoc_member_order = "bysource"

# The encoding of source files.
# source_encoding = 'utf-8-sig'

# The master toctree document.
master_doc = "index"

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# Thes are both the full X.Y.Z version, including alpha/beta/rc tags.
# We think Sphinx probably wants both to be defined.
release = version

project = "DataRobot Python Client"
copyright = "2019, DataRobot, Inc"


# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
# language = None

# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
# today = ''
# Else, today_fmt is used as the format for a strftime call.
# today_fmt = '%B %d, %Y'

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ["_build", "**/.ipynb_checkpoints"]

# The reST default role (used for this markup: `text`) to use for all documents.
# default_role = None

# If true, '()' will be appended to :func: etc. cross-reference text.
# add_function_parentheses = True

# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
# add_module_names = True

# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
# show_authors = False

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"

# A list of ignored prefixes for module index sorting.
# modindex_common_prefix = []


# -- Options for HTML output ---------------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
import sphinx_rtd_theme

html_theme = "sphinx_rtd_theme"
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
# html_theme_options = {}

# Add any paths that contain custom themes here, relative to this directory.
# html_theme_path = []

# The name for this set of Sphinx documents.  If None, it defaults to
# "<project> v<release> documentation".
# html_title = None

# A shorter title for the navigation bar.  Default is the same as html_title.
# html_short_title = None

# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
# html_logo = None

# The name of an image file (within the static path) to use as favicon of the
# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
# html_favicon = None

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
html_extra_path = ["_html"]

# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
# html_last_updated_fmt = '%b %d, %Y'

# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
# html_use_smartypants = True

# Custom sidebar templates, maps document names to template names.
# html_sidebars = {}

# Additional templates that should be rendered to pages, maps page names to
# template names.
# html_additional_pages = {}

# If false, no module index is generated.
# html_domain_indices = True

# If false, no index is generated.
# html_use_index = True

# If true, the index is split into individual pages for each letter.
# html_split_index = False

# If true, links to the reST sources are added to the pages.
# html_show_sourcelink = True

# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
# html_show_sphinx = True

# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
# html_show_copyright = True

# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it.  The value of this option must be the
# base URL from which the finished HTML is served.
# html_use_opensearch = ''

# This is the file name suffix for HTML files (e.g. ".xhtml").
# html_file_suffix = None

# Output file base name for HTML help builder.
htmlhelp_basename = "DataRobotPythonDoc"


# -- Options for LaTeX output --------------------------------------------------

# latex_elements = {
#     The paper size ('letterpaper' or 'a4paper').
#     'papersize': 'letterpaper',

#     The font size ('10pt', '11pt' or '12pt').
#     'pointsize': '10pt',

#     Additional stuff for the LaTeX preamble.
#     'preamble': '',
# }

fh = open("latex_preamble.tex", "r+")
PREAMBLE = fh.read()
fh.close()
latex_elements = {
    "inputenc": "",
    "utf8extra": "",
    "preamble": PREAMBLE,
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
    (
        "index",
        f"DataRobotPython_{version}_Docs.tex",
        "DataRobot Python API Documentation",
        "DataRobot, Inc.",
        "manual",
    ),
]

# The name of an image file (relative to this directory) to place at the top of
# the title page.
# latex_logo = None

# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
# latex_use_parts = False

# If true, show page references after internal links.
# latex_show_pagerefs = False

# If true, show URL addresses after external links.
# latex_show_urls = False

# Documents to append as an appendix to all manuals.
# latex_appendices = []

# If false, no module index is generated.
# latex_domain_indices = True


# -- Options for manual page output --------------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
    ("index", "datarobotpython", "DataRobot Python API Documentation", ["DataRobot, Inc."], 1)
]

# If true, show URL addresses after external links.
# man_show_urls = False


# -- Options for Texinfo output ------------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
#  dir menu entry, description, category)
texinfo_documents = [
    (
        "index",
        "DataRobotPython",
        "DataRobot Python API Documentation",
        "DataRobot, Inc.",
        "DataRobotPython",
        "One line description of project.",
        "Miscellaneous",
    ),
]

# Documents to append as an appendix to all manuals.
# texinfo_appendices = []

# If false, no module index is generated.
# texinfo_domain_indices = True

# How to display URL addresses: 'footnote', 'no', or 'inline'.
# texinfo_show_urls = 'footnote'


def setup(app):
    app.add_css_file("css/tight_table.css")




###########################################################################
#          auto-created readthedocs.org specific configuration            #
###########################################################################


#
# The following code was added during an automated build on readthedocs.org
# It is auto created and injected for every build. The result is based on the
# conf.py.tmpl file found in the readthedocs.org codebase:
# https://github.com/rtfd/readthedocs.org/blob/main/readthedocs/doc_builder/templates/doc_builder/conf.py.tmpl
#
# Note: this file shouldn't rely on extra dependencies.

import importlib
import sys
import os.path

# Borrowed from six.
PY3 = sys.version_info[0] == 3
string_types = str if PY3 else basestring

from sphinx import version_info

# Get suffix for proper linking to GitHub
# This is deprecated in Sphinx 1.3+,
# as each page can have its own suffix
if globals().get('source_suffix', False):
    if isinstance(source_suffix, string_types):
        SUFFIX = source_suffix
    elif isinstance(source_suffix, (list, tuple)):
        # Sphinx >= 1.3 supports list/tuple to define multiple suffixes
        SUFFIX = source_suffix[0]
    elif isinstance(source_suffix, dict):
        # Sphinx >= 1.8 supports a mapping dictionary for multiple suffixes
        SUFFIX = list(source_suffix.keys())[0]  # make a ``list()`` for py2/py3 compatibility
    else:
        # default to .rst
        SUFFIX = '.rst'
else:
    SUFFIX = '.rst'

# Add RTD Static Path. Add to the end because it overwrites previous files.
if not 'html_static_path' in globals():
    html_static_path = []
if os.path.exists('_static'):
    html_static_path.append('_static')

# Add RTD Theme only if they aren't overriding it already
using_rtd_theme = (
    (
        'html_theme' in globals() and
        html_theme in ['default'] and
        # Allow people to bail with a hack of having an html_style
        'html_style' not in globals()
    ) or 'html_theme' not in globals()
)
if using_rtd_theme:
    html_theme = 'sphinx_rtd_theme'
    html_style = None
    html_theme_options = {}


# This following legacy behavior will gradually be sliced out until its deprecated and removed.
# Skipped for Sphinx 6+
# Skipped by internal Feature flag SKIP_SPHINX_HTML_THEME_PATH
# Skipped by all new projects since SKIP_SPHINX_HTML_THEME_PATH's introduction (jan 2023)
if (
        using_rtd_theme
        and version_info < (6,0)
        and not False
    ):
    theme = importlib.import_module('sphinx_rtd_theme')
    if 'html_theme_path' in globals():
        html_theme_path.append(theme.get_html_theme_path())
    else:
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# Define websupport2_base_url and websupport2_static_url
if globals().get('websupport2_base_url', False):
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    websupport2_static_url = 'https://assets.readthedocs.com/'


#Add project information to the template context.
context = {
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    'gitlab_version': 'early-access',
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# For sphinx >=1.8 we can use html_baseurl to set the canonical URL.
# https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_baseurl
if version_info >= (1, 8):
    if not globals().get('html_baseurl'):
        html_baseurl = context['canonical_url']
    context['canonical_url'] = None





if 'html_context' in globals():
    
    html_context.update(context)
    
else:
    html_context = context

# Add custom RTD extension
if 'extensions' in globals():
    # Insert at the beginning because it can interfere
    # with other extensions.
    # See https://github.com/rtfd/readthedocs.org/pull/4054
    extensions.insert(0, "readthedocs_ext.readthedocs")
else:
    extensions = ["readthedocs_ext.readthedocs"]

# Add External version warning banner to the external version documentation
if 'tag' == 'external':
    extensions.insert(1, "readthedocs_ext.external_version_warning")
    readthedocs_vcs_url = 'None'
    readthedocs_build_url = 'https://readthedocs.com/projects/datarobot-public-api-client/builds/1403244/'

project_language = 'en'

# User's Sphinx configurations
language_user = globals().get('language', None)
latex_engine_user = globals().get('latex_engine', None)
latex_elements_user = globals().get('latex_elements', None)

# Remove this once xindy gets installed in Docker image and XINDYOPS
# env variable is supported
# https://github.com/rtfd/readthedocs-docker-images/pull/98
latex_use_xindy = False

chinese = any([
    language_user in ('zh_CN', 'zh_TW'),
    project_language in ('zh_CN', 'zh_TW'),
])

japanese = any([
    language_user == 'ja',
    project_language == 'ja',
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if chinese:
    latex_engine = latex_engine_user or 'xelatex'

    latex_elements_rtd = {
        'preamble': '\\usepackage[UTF8]{ctex}\n',
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    latex_elements = latex_elements_user or latex_elements_rtd
elif japanese:
    latex_engine = latex_engine_user or 'platex'

# Make sure our build directory is always excluded
exclude_patterns = globals().get('exclude_patterns', [])
exclude_patterns.extend(['_build'])

[rtd-command-info] start-time: 2023-04-10T16:47:55.481093Z, end-time: 2023-04-10T16:48:31.833647Z, duration: 36, exit-code: 0
python -m sphinx -T -E -b html -d _build/doctrees -D language=en . $READTHEDOCS_OUTPUT/html
Running Sphinx v1.8.3
loading translations [en]... done
making output directory...
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 41 source files that are out of date
updating environment: 41 added, 0 changed, 0 removed
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You have imported from the _experimental directory.
This directory is used for unreleased datarobot features.
Unless you specifically know better, you don't have the access to use this functionality in the app, so this code will not work.
reading sources... [  7%] examples/index
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generating indices... genindex py-modindex
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copying static files... done
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dumping object inventory... done
build succeeded.

The HTML pages are in ../_readthedocs/html.
Updating searchtools for Read the Docs search...

[rtd-command-info] start-time: 2023-04-10T16:48:32.039039Z, end-time: 2023-04-10T16:49:08.579347Z, duration: 36, exit-code: 0
python -m sphinx -T -E -b latex -d _build/doctrees -D language=en . $READTHEDOCS_OUTPUT/pdf
Running Sphinx v1.8.3
loading translations [en]... done
making output directory...
building [mo]: targets for 0 po files that are out of date
building [latex]: all documents
updating environment: 41 added, 0 changed, 0 removed
reading sources... [  2%] CHANGES
reading sources... [  4%] autodoc/api_reference
You have imported from the _experimental directory.
This directory is used for unreleased datarobot features.
Unless you specifically know better, you don't have the access to use this functionality in the app, so this code will not work.
reading sources... [  7%] examples/index
reading sources... [  9%] getting_started
reading sources... [ 12%] index
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reading sources... [ 78%] reference/modeling/spec/index
reading sources... [ 80%] reference/modeling/spec/monotonic_constraints
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reading sources... [ 87%] reference/modeling/spec/unsupervised_anomaly
reading sources... [ 90%] reference/modeling/spec/unsupervised_clustering
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checking consistency... done
processing DataRobotPython_3.2.0b0_Docs.tex...index getting_started reference/index reference/data/index reference/data/dataset reference/data/database_connectivity reference/data/feature_discovery reference/modeling/index reference/modeling/project reference/modeling/model reference/modeling/blueprint reference/modeling/spec/index reference/modeling/spec/datetime_partition reference/modeling/spec/time_series reference/modeling/spec/visualai reference/modeling/spec/unsupervised_anomaly reference/modeling/spec/unsupervised_clustering reference/modeling/spec/segmented_modeling reference/modeling/spec/custom_task reference/modeling/spec/monotonic_constraints reference/modeling/spec/binary_data reference/modeling/insights/index reference/modeling/insights/prediction_explanations reference/modeling/insights/rating_table reference/modeling/insights/automated_documentation reference/modeling/insights/external_testset reference/modeling/job reference/modeling/prime reference/modeling/model_recommendation reference/predictions/index reference/predictions/predict_job reference/predictions/batch_predictions reference/mlops/index reference/mlops/deployment reference/mlops/custom_model reference/admin/index reference/admin/credentials reference/admin/sharing autodoc/api_reference examples/index CHANGES 
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writing... done
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copying images... [ 40%] reference/modeling/spec/images/visualai/sample2.png
copying images... [ 60%] reference/modeling/spec/images/visualai/activation_map1.png
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copying TeX support files...
done
build succeeded.

The LaTeX files are in ../_readthedocs/pdf.
Run 'make' in that directory to run these through (pdf)latex
(use `make latexpdf' here to do that automatically).
WARNING: Missing searchtools: /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/early-access/_readthedocs/pdf/_static/searchtools.js

[rtd-command-info] start-time: 2023-04-10T16:49:09.466356Z, end-time: 2023-04-10T16:49:09.527348Z, duration: 0, exit-code: 0
cat latexmkrc
$latex = 'latex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$pdflatex = 'pdflatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$lualatex = 'lualatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
$xelatex = 'xelatex --no-pdf ' . $ENV{'LATEXOPTS'} . ' %O %S';
$makeindex = 'makeindex -s python.ist %O -o %D %S';
add_cus_dep( "glo", "gls", 0, "makeglo" );
sub makeglo {
 return system( "makeindex -s gglo.ist -o '$_[0].gls' '$_[0].glo'" );
}

[rtd-command-info] start-time: 2023-04-10T16:49:09.878072Z, end-time: 2023-04-10T16:49:27.869412Z, duration: 17, exit-code: 0
latexmk -r latexmkrc -pdf -f -dvi- -ps- -jobname=datarobot-public-api-client -interaction=nonstopmode


... (truncated) ...

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\T1/pcr/m/sl/10 models.Model.get_feature_effects_multiclass() \T1/ptm/m/n/10 or
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[]\T1/pcr/m/sl/10 datarobot.models.Deployment.get_predictions_by_forecast_date_
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Chapter 1.
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Chapter 2.
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[][][]\T1/pcr/m/sl/10 Dataset.create_from_url[][] \T1/ptm/m/n/10 takes .csv dat
a from a URL. If you have set

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[][][]\T1/pcr/m/sl/10 Dataset.create_from_data_source[][] \T1/ptm/m/n/10 takes 
data from a data source. If you have set
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[]\T1/ptm/m/n/10 For mul-ti-class mod-els use \T1/pcr/m/n/10 request_feature_ef
fect_multiclass \T1/ptm/m/n/10 and
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[]\T1/ptm/m/n/10 For mul-ti-class mod-els, you can get a list of per-class lift
 charts us-ing the \T1/pcr/m/n/10 Model \T1/ptm/m/n/10 method
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[]\T1/ptm/m/n/10 After con-fig-ur-ing back-tests, you can set \T1/pcr/m/n/10 us
e_project_settings \T1/ptm/m/n/10 to \T1/pcr/m/n/10 True \T1/ptm/m/n/10 in call
s to [][]\T1/pcr/m/sl/10 Model.
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[]\T1/ptm/m/n/10 For date-time par-ti-tioned model you can re-trieve the Ac-cu-
racy over Time plot. To do so use
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[][][]\T1/pcr/m/sl/10 datarobot.FeatureSettings[][] \T1/ptm/m/n/10 con-struc-to
r re-ceives \T1/ptm/m/it/10 fea-ture_name \T1/ptm/m/n/10 and set-tings. For now
 set-tings
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[]\T1/ptm/m/n/10 Enough rows of his-tor-i-cal data must be pro-vided to cover t
he span of the ef-fec-tive Fea-ture

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\T1/ptm/m/n/10 Deriva-tion Win-dow (which may be longer than the pro-jec-t’s 
Fea-ture Deriva-tion Win-dow de-

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\T1/ptm/m/n/10 pend-ing on the dif-fer-enc-ing set-tings cho-sen). The ef-fec-t
ive Fea-ture Deriva-tion Win-dow of any

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\T1/ptm/m/n/10 model can be checked via the \T1/pcr/m/n/10 effective_feature_de
rivation_window_start \T1/ptm/m/n/10 and
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[]\T1/ptm/m/n/10 Prediction in-ter-vals can be cal-cu-lated for a [][]\T1/pcr/m
/sl/10 DatetimeModel[][] \T1/ptm/m/n/10 us-ing the [][]\T1/pcr/m/sl/10 Datetime
Model.

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[]\T1/ptm/m/n/10 Prediction in-ter-vals are also sup-ported for time se-ries mo
del ex-port. See the op-tional
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[55] [56] [57] [58 <./sample1.png> <./sample2.png>] [59] [60 <./activation_map1
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[] 
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[]\T1/ptm/m/n/10 Upload a pre-dic-tion dataset that con-tains la-bel col-umn(s)
, re-quest com-pute ex-ter-nal test on one of
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro-cesses=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=Non
e\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-


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\T1/ptm/m/it/10 rics=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project=None\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 mono-

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[]\T1/ptm/m/it/10 cluster_name_mappings: List[Tuple[str, str]]\T1/pcr/m/n/10 ) 
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tr\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 project_id: str, model_id: str, clus-

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\T1/ptm/m/it/10 ter_name_mappings: List[Tuple[str, str]]\T1/pcr/m/n/10 ) $\OMS/
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy
/m/n/10 !$
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[]\T1/ptm/m/n/10 Upload a pre-dic-tion dataset that con-tains the tar-get col-u
mn \T1/pcr/m/n/10 PredictionDataset.
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[]\T1/ptm/m/n/10 If you just want to get gen-er-ated pre-dic-tions from a Pre-d
ic-tJob, you can use the \T1/pcr/m/n/10 PredictJob.
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[]\T1/ptm/m/n/10 The method for cre-at-ing job def-i-ni-tions is iden-ti-cal to
 the ex-ist-ing [][]\T1/pcr/m/sl/10 datarobot.models.
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[]\T1/ptm/m/n/10 Alternatively, you can use a short-hand ver-sion with [][]\T1/
pcr/m/sl/10 datarobot.models.
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[]\T1/ptm/m/n/10 Before ini-ti-at-ing the model re-place-ment re-quest, it is u
su-ally a good idea to use the
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[]\T1/ptm/m/n/10 Refer to [][]\T1/pcr/m/sl/10 ExecutionEnvironment[][] \T1/ptm/
m/n/10 for prop-er-ties of the ex-e-cu-tion en-vi-ron-ment ob-ject and
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[]\T1/ptm/m/it/10 weights: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seed: Op-tional[int] = N
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\T1/ptm/m/it/10 ma-jor-ity_downsampling_rate: Op-tional[float]

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\T1/ptm/m/it/10 ex-po-sure: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 ac-cu-

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\T1/ptm/m/it/10 racy_optimized_mb: Op-tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sca-le-out_modeling_mode: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 events_count: Op-tional[
str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing_feat
urelist_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 only_include_monotonic_b
lueprints:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 lowed_pairwise_interaction_groups: Op-

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\T1/ptm/m/it/10 tional[List[Tuple[str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ...]]] = 
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blend_best_models: Op-tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 scor-ing_code_only: Op-tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-sider_blenders_in_reco
mmendation:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 min_secondary_validation_model_count:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 shap_only
_mode:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-topi-


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\T1/ptm/m/it/10 lot_data_sampling_method: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 run_leakage_removed_feat
ure_list:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-topi-


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\T1/ptm/m/it/10 lot_with_feature_discovery: Op-

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\T1/ptm/m/it/10 tional[bool] = False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_discovery_supervised_feature_reduction:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-po-ne
n-

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-ter-nal_time_series_bas
eline_dataset_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fair-ness_metrics_set: O
p-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fair-ness_threshold: Op-
tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 bias_mitigation_feature_
name: Op-

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-

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\T1/ptm/m/it/10 clude_bias_mitigation_feature_as_predictor_variable:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-

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\T1/ptm/m/it/10 fault_monotonic_increasing_featurelist_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-

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\T1/ptm/m/it/10 fault_monotonic_decreasing_featurelist_id:
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[]\T1/ptm/m/n/10 (New in ver-sion v2.23) one of

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[]\T1/ptm/m/n/10 (New in ver-sion v2.24) Met-ric to use for cal-

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\T1/ptm/m/n/10 cu-lat-ing fair-ness. Can be one of \T1/pcr/m/n/10 proportionalP
arity\T1/ptm/m/n/10 , \T1/pcr/m/n/10 equalParity\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 predictionBalance\T1/ptm/m/n/10 , \T1/pcr/m/n/10 trueFavorableAn
dUnfavorableRateParity \T1/ptm/m/n/10 or
[154]
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[]\T1/ptm/m/it/10 status:

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\T1/ptm/m/it/10 datarobot.enums.AnomalyAssessmentStatus,

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\T1/ptm/m/it/10 sta-

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\T1/ptm/m/it/10 tus_details:

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\T1/ptm/m/it/10 str,

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\T1/ptm/m/it/10 start_date:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 end_date:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[float],

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 view_location:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 delete_location:

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\T1/ptm/m/it/10 str, lat-

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\T1/ptm/m/it/10 est_explanations_location:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 **record_kwargs\T1/pcr/m/n/10 ) 
[155] [156]
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[]\T1/ptm/m/n/10 The sta-tus of the in-sight. One of \T1/pcr/m/n/10 datarobot.e
nums.

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[]\T1/ptm/m/it/10 project_id: str, model_id: str, back-test: Union[int,

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\T1/ptm/m/it/10 typ-ing_extensions.Literal[<DATA_SUBSET.HOLDOUT:

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\T1/ptm/m/it/10 'hold-out'>][holdout], None] = None, source:

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\T1/ptm/m/it/10 Union[typing_extensions.Literal[<SOURCE_TYPE.TRAINING: 'train-

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\T1/ptm/m/it/10 ing'>][training], typ-ing_extensions.Literal[<SOURCE_TYPE.VALID
ATION:
[157]
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[]\T1/ptm/m/n/10 Filter by \T1/ptm/m/it/10 sta-tus \T1/ptm/m/n/10 == Anoma-lyAs
sess-mentSta-

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[]\T1/ptm/m/it/10 project_id: str, model_id: int, back-test: Union[int, typ-

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\T1/ptm/m/it/10 ing_extensions.Literal[<DATA_SUBSET.HOLDOUT: 'hold-

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\T1/ptm/m/it/10 out'>][holdout], None], source: Union[typing_extensions.Literal
[<SOURCE_TYPE.TRAINING:

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\T1/ptm/m/it/10 'train-ing'>][training], typ-ing_extensions.Literal[<SOURCE_TYP
E.VALIDATION:

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.anomaly
_assessment.AnomalyAssessmentPredictionsPreview 

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.anomaly
_assessment.AnomalyAssessmentExplanations 

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[]\T1/ptm/m/it/10 start_date: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 end_date: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 points_count: Op-tional[
int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[158]
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[]\T1/ptm/m/it/10 regions: List[datarobot.models.anomaly_assessment.PreviewBin]
,

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\T1/ptm/m/it/10 pre-dic-tion_threshold: float = 0.0\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.anomaly_assessment.RegionExplanationsData 

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[]

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[]\T1/ptm/m/it/10 shap_base_value:

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\T1/ptm/m/it/10 float,

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\T1/ptm/m/it/10 data:

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\T1/ptm/m/it/10 List[datarobot.models.anomaly_assessment.DataPoint],

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\T1/ptm/m/it/10 start_date:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 end_date:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 count:

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\T1/ptm/m/it/10 **record_kwargs\T1/pcr/m/n/10 ) 
[159] [160]
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[]\T1/ptm/m/it/10 start_date:

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\T1/ptm/m/it/10 str,

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\T1/ptm/m/it/10 end_date:

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 view_bins:

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\T1/ptm/m/it/10 List[datarobot.models.anomaly_assessment.PreviewBin],

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\T1/ptm/m/it/10 **record_kwargs\T1/pcr/m/n/10 ) 
[161]
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[]\T1/ptm/m/it/10 max_prediction_threshold: float = 0.0\T1/pcr/m/n/10 ) $\OMS/c
msy/m/n/10 !$

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[]\T1/ptm/m/n/10 greater or

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 out-put_settings: Op-tio
nal[OutputSettings] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 thresh-old_low: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 pre-dic-tion_warning_enabled:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude
_prediction_status: bool = False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 skip_drift_tracking: bool = False\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 pre-dic-tion_instance: Op-

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\T1/ptm/m/it/10 tional[PredictionInstance] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 abort_on_error: bool = True\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 col-umn_names_remapping: Op-tional[Dict[str\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 str]] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-
[162] [163] [164] [165]
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\T1/ptm/m/it/10 time-series_settings: Time-
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[]\T1/ptm/m/it/10 deployment:

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\T1/ptm/m/it/10 De-ploy-ment,

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\T1/ptm/m/it/10 in-take_path:

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\T1/ptm/m/it/10 Union[str,

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\T1/ptm/m/it/10 pd.DataFrame,

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\T1/ptm/m/it/10 io.IOBase],

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\T1/ptm/m/it/10 str, time-

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\T1/ptm/m/it/10 series_settings:

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\T1/ptm/m/it/10 Time-Series-Set-

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\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Batch-Pre-dic-
[168] [169]
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[]\T1/ptm/m/n/10 [string] The URL for the pre-dic-tion dataset (e.g.:

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[]\T1/ptm/m/n/10 [string] The URL for the scored dataset (e.g.:

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[]\T1/ptm/m/n/10 [string] The URL for the pre-dic-tion dataset (e.g.:

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[]\T1/ptm/m/n/10 [string] The URL for the scored dataset (e.g.:

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[]\T1/ptm/m/it/10 batch_prediction_job_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/1
0 !$

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\T1/ptm/m/n/10 datarobot.models.batch_prediction_job.BatchPredictionJob 
[170]
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[]\T1/ptm/m/it/10 batch_prediction_job_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/1
0 !$ \T1/ptm/m/n/10 datarobot.models.batch_prediction_job.BatchPredictionJob 

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[]\T1/ptm/m/it/10 statuses: Op-tional[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/c
msy/m/n/10 !$

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\T1/ptm/m/it/10 batch_prediction_job=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-ated: Op-tional[str] =

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated_by=None\T1/ptm/
m/n/10 ,

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 last_started_job_status:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 last_scheduled_run_time:
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[]\T1/ptm/m/it/10 batch_prediction_job_definition_id: str\T1/pcr/m/n/10 ) $\OMS
/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 nodes: List[Dict[str, str]], edges:

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.blueprint
.ModelBlueprintChart 
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[]\T1/ptm/m/it/10 file_path: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-en-dar_nam
e: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mul-ti-series_id_columns: Op-tional[List[str]] = None\T1/pcr/m/
n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/n/10 Once the Cal-en-darFile has been cre-ated, pass its ID with th
e
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[]\T1/ptm/m/it/10 dataset_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_versi
on_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-en-

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\T1/ptm/m/it/10 dar_name: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mul-ti-series_id_columns: Op-

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\T1/ptm/m/it/10 Op-tional[bool] = False\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.calendar_file.CalendarFile 

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[]\T1/ptm/m/n/10 Once the Cal-en-darFile has been cre-ated, pass its ID with th
e
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[]\T1/ptm/m/it/10 country_code: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_date:


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\T1/ptm/m/it/10 date-time.datetime\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_date:

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\T1/ptm/m/it/10 date-time.datetime\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.calendar_file.CalendarFile 
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[]\T1/ptm/m/it/10 entity_id:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 u-ment_type:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tem-plate_id:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 filepath:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 tional[datetime.datetime]
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[]\T1/ptm/m/n/10 [str or None] Type of au-to-mated doc-u-ment. You can spec-ify
:

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 List[datarobot.models.au
tomated_documentation.DocumentOption] 
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[]\T1/ptm/m/n/10 Required at-tributes to re-quest doc-u-ment gen-er-a-tion: \T1
/pcr/m/n/10 document_type\T1/ptm/m/n/10 , \T1/pcr/m/n/10 entity_id\T1/ptm/m/n/1
0 , and
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[]

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[]\T1/ptm/m/it/10 max_unaggregated_class_values:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 min_class_support:

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\T1/ptm/m/it/10 cluded_from_aggregation:

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\T1/ptm/m/it/10 Op-tional[List[str]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ag-gre-ga-

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\T1/ptm/m/it/10 tion_class_name: Op-
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[]\T1/ptm/m/n/10 Authentication to-ken type: To-ken, Bearer.
[191]
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[]\T1/ptm/m/it/10 client: datarobot.rest.RESTClientObject\T1/pcr/m/n/10 ) $\OMS
/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Op-

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[]\T1/ptm/m/it/10 auth: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end-point: str\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 con-nect_timeout:

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\T1/ptm/m/it/10 Op-tional[int] = 6.05\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-ify: 
bool = True\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 user_agent_suffix: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_retries: Union[int\T1/ptm/m/n/10 , \T1/ptm/m/it/10 url-lib3
.util.retry.Retry\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-

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\T1/ptm/m/it/10 ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_sta
rt_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_t
ype=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_n_clusters_dynamically_determined=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 n_clusters=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 has_empty_clust
ers=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_project_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 cluster_name_mappings: List[Tuple[str, str]]\T1/pcr/m/n/10 ) 
$\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 current_name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 new_name: s
tr\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 project_id: str, model_id: str, clus-

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\T1/ptm/m/it/10 ter_name_mappings: List[Tuple[str, str]]\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy
/m/n/10 !$
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\T1/ptm/m/it/10 ator_username:

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\T1/ptm/m/it/10 name:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str]

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\T1/ptm/m/it/10 =

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\T1/ptm/m/it/10 sec-

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\T1/ptm/m/it/10 tions:

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\T1/ptm/m/it/10 tional[List[datarobot.models.compliance_doc_template.Section]]

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\T1/ptm/m/it/10 =
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[]\T1/ptm/m/it/10 template_type: Op-tional[datarobot.enums.ComplianceDocTemplat
eType]
[197]
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[]\T1/ptm/m/it/10 name: str, sec-tions: List[datarobot.models.compliance_doc_te
mplate.Section]\T1/pcr/m/n/10 )

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[]\T1/ptm/m/it/10 name: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
ec-tions: Op-tional[List[datarobot.models.compliance_doc_template.Section]]
[198]
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[]\T1/ptm/m/it/10 source:

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\T1/ptm/m/it/10 datarobot.enums.CHART_DATA_SOURCE\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data:

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\T1/ptm/m/it/10 datarobot.models.confusion_chart.ConfusionChartRawData\T1/ptm/m
/n/10 ,

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[]

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[]\T1/pcr/m/n/10 [ [ True Negative, False Positive ], [ False Negative, True
[199] [200] [201]
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[]\T1/ptm/m/it/10 name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 aws_access_key_id: 
Op-tional[str] = None\T1/ptm/m/n/10 ,
[202]
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\T1/ptm/m/it/10 file_path\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 file_source\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-

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\T1/ptm/m/it/10 ated_at=None\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/n/10 target type of the cus-tom in-fer-ence model.

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\T1/ptm/m/it/10 datarobot.TARGET_TYPE.MULTICLASS\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 datarobot.TARGET_TYPE.UNSTRUCTURED\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 Target fea-ture name; it is op-tional(ignored if pro-vided)

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[]\T1/ptm/m/n/10 Determines
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 tar-get_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guag
e=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-

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\T1/ptm/m/it/10 tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i-tive_class_labe
l=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 neg-a-tive_class_label=None\T1/ptm/m/n/1
0 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cla
ss_labels=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_labels_file=None\T1/ptm/m/
n/10 ,
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[]\T1/ptm/m/n/10 target type of the cus-

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\T1/ptm/m/n/10 tom in-fer-ence model. Val-ues: [\T1/ptm/m/it/10 datarobot.TARGE
T_TYPE.BINARY\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 datarobot.TARGET_TYPE.REGRESSION\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 datarobot.TARGET_TYPE.MULTICLASS\T1/ptm/m/n/10 ,

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[]

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[]\T1/ptm/m/it/10 name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guage=None\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
tar-get_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i-

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\T1/ptm/m/it/10 tive_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 neg-a-tiv
e_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_threshold=None\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 the type of the im-age, ei-ther CUS-TOM_MODEL_IMAGE_TYPE.CUSTO
M_MODEL_IMAGE

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\T1/ptm/m/n/10 if the test-ing at-tempt is us-ing a Cus-tom-Mod-e-lIm-age as it
s model or CUS-

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[]\T1/ptm/m/n/10 Determines

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/n/10 ,
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[]
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[]\T1/ptm/m/n/10 Determines
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\T1/ptm/m/it/10 folder_path=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 files=None\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 net-work_egress_policy=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max-i-mum_memory=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 repli-cas
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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.txt”,

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\T1/ptm/m/n/10 “-folder/file2.txt”)] or [“/home/user/Documents/myModel/fi
le1.txt”,

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[]

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 base_environm
ent_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 files_to_delete=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 net-work_e
gress_policy=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max-i-mum_memory=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 repli-cas
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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.txt”,

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\T1/ptm/m/n/10 “-folder/file2.txt”)] or [“/home/user/Documents/myModel/fi
le1.txt”,

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[]

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 ,
[215] [216] [217]
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 pro-gram-ming_language=None\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 re-
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[]\T1/ptm/m/it/10 execution_environment_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dock
er_context_path\T1/ptm/m/n/10 , \T1/ptm/m/it/10 la-bel=None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 de-scrip-
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\T1/ptm/m/n/10 See datarobot.enums.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS
[221]
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[]\T1/ptm/m/it/10 id: str, tar-get_type: datarobot.enums.CUSTOM_TASK_TARGET_TYP
E,

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\T1/ptm/m/it/10 lat-est_version: Op-tional[datarobot.models.custom_task_version
.CustomTaskVersion],

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\T1/ptm/m/it/10 datarobot.enums.Enum, cre-ated_by: str, cal-i-brate_predictions
:

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\T1/ptm/m/n/10 tween 0 and 1 by DR. only ap-plies to cus-tom es-ti-ma-tors with
 tar-get type
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[]\T1/ptm/m/it/10 data: Union[Dict[str, Any], List[Dict[str, Any]]],

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\T1/ptm/m/it/10 keep_attrs: Op-tional[Iterable[str]] = None\T1/pcr/m/n/10 ) $\O
MS/cmsy/m/n/10 !$
[223]
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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-i-brate_
predictions: Op-tional[bool] = None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 the tar-get typed

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[]\T1/ptm/m/n/10 whether anomaly pre-dic-tions should be

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\T1/ptm/m/n/10 cal-i-brated to be be-tween 0 and 1 by DR. if None, uses de-faul
t value

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\T1/ptm/m/n/10 from DR app (True). only ap-plies to cus-tom es-ti-ma-tors with 
tar-get type
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 file_name\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 bel\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated_at\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 is_frozen\T1/ptm/m/n/10 , \T1/ptm/m/it/10 items\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 base_environment_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max-i-
mum_memory=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 is_major_update=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 folder_pat
h=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-

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[]\T1/ptm/m/it/10 custom_task_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 base_environme
nt_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_major_update=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 folder_pat
h=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 quired_metadata_values=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max
-i-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/it/10 class_name: str, canon-i-cal_name: str, files: List[str]\T1/p
cr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[231] [232] [233] [234]
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[]\T1/ptm/m/n/10 [str] the full JDBC url, for ex-am-ple \T1/ptm/m/it/10 jdbc:po
stgresql://my.dbaddress.org:5432/my_db\T1/ptm/m/n/10 . 

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[]\T1/ptm/m/n/10 [str] op-tional, the full JDBC url, for ex-am-ple
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[]\T1/ptm/m/it/10 data: Union[Dict[str, Any], List[Dict[str, Any]]],

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\T1/ptm/m/it/10 keep_attrs: Op-tional[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/c
msy/m/n/10 !$

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[]\T1/ptm/m/it/10 data_source_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 data_source_type:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 canon-i-c
al_name: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ator: Op-tional[str]
 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 up-dated:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 params: Op-

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\T1/ptm/m/it/10 tional[datarobot.models.data_source.DataSourceParameters] =
[237]
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[]\T1/ptm/m/it/10 data_source_type: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 canon-i
-cal_name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 params:
[238]
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[]\T1/ptm/m/it/10 canonical_name: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 params: Op-
[239] [240] [241] [242]
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories: Op-tional[
List[str]] = None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 data_frame: Op-

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\T1/ptm/m/it/10 tional[pandas.core.frame.DataFrame] =
[243]
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[]\T1/ptm/m/it/10 url: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 do_snapshot: Op-tion
al[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 per-

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pass-word: Op-tional[str
] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 do_snapshot: Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 per-
[244] [245] [246] [247]
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[]\T1/ptm/m/it/10 order_by: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/
n/10 !$ \T1/ptm/m/n/10 List[datarobot.models.feature.DatasetFeature] 

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[]\T1/ptm/m/it/10 offset: Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 limit: Op-tional[int] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 or-der_by: Op-tional[str] 
= None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Gen-er-a-
[248]
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[]\T1/ptm/m/it/10 name: str, fea-tures: List[str]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/
n/10 !$ \T1/ptm/m/n/10 datarobot.models.featurelist.DatasetFeaturelist 
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\T1/ptm/m/it/10 tional[pandas.core.frame.DataFrame]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 records: Op-

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\T1/ptm/m/it/10 tional[List[Dict[str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 Any]]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories: Op-

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\T1/ptm/m/it/10 tional[List[str]] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 read_timeout: int = 600\T1/ptm/m/n/10 ,
[251]
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\T1/ptm/m/it/10 user-name: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-den-tial_data: Op-tional[Dict[str\T1/ptm/m/n/10 ,
[252]
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\T1/ptm/m/it/10 ated_by: str, cre-ated_at: str, data_source_type: str,

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\T1/ptm/m/it/10 is_data_engine_eligible: bool, last_modification_date:

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\T1/ptm/m/it/10 pro-cess-ing_state: str, data_persisted: Op-tional[bool]

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\T1/ptm/m/it/10 = None, data_engine_query_id: Op-tional[str] = None,

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\T1/ptm/m/it/10 Op-tional[List[datarobot.models.dataset.FeatureTypeCount]] =
[253]
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[]\T1/ptm/m/n/10 the uri to data-source like: - ‘-file_name.csv’ - ‘[][]$
jdbc : DATA _
[254] [255]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: s
tr\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dataset_version_id: Op-tional[str] =

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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.prediction_dataset.PredictionDataset 

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[]\T1/ptm/m/it/10 sourcedata: Union[str, pan-das.core.frame.DataFrame,

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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check: Op-tional[bool] =
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fore-

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\T1/ptm/m/it/10 cast_distance\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 res-

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\T1/ptm/m/it/10 tions\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-

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\T1/ptm/m/it/10 test_metadata\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_metadata\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-

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\T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_statuses\T1/pcr/m/n/10 ) 
[257]
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[]\T1/ptm/m/n/10 Status back-test/holdout train-ing. One of \T1/pcr/m/n/10 data
robot.enums.

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[]\T1/ptm/m/n/10 Status back-test/holdout val-i-da-tion. One of \T1/pcr/m/n/10 
datarobot.enums.

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[]\T1/ptm/m/n/10 A list of \T1/pcr/m/n/10 datarobot.enums.
[258]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 start_date\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 end_date\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 res-o-lu-tion\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 bins\T1/ptm/m/n/10 , \T1/ptm/m/it/10 statis-

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\T1/ptm/m/it/10 tics\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-en-
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 start_date\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 end_date\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 bins\T1/pcr/m/n/10 ) 

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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 res-

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\T1/ptm/m/it/10 tions\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-

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[260]
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\T1/ptm/m/it/10 own-ers: Op-tional[Dict[str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 Any
]] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-
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_id:
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[]
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-tributes, see

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[274]
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[]\T1/ptm/m/n/10 [bool] If tar-get drift track-ing is en-abled for this de-ploy
-ment.

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\T1/ptm/m/n/10 To cre-ate or up-date ex-ist-ing ‘’-tar-get_drift’’ set-
tings, see
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_time: Op-tional[date
time.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-e-cu-

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\T1/ptm/m/it/10 tion_time_quantile: Op-tional[float] = None\T1/ptm/m/n/10 , \T1
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tetime.datetime] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_time: Op-tional[dateti
me.datetime] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 bucket_size: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it
/10 quan-tile: Op-
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: Op-tional[str]
 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_time:

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_classes: Op-tion
al[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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lanations: bool = False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit: in
t = 100\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: int = 0\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$

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\T1/ptm/m/it/10 tile: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 t
hresh-old: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 period=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\T1/p
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[]\T1/ptm/m/it/10 period=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\T1/p
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[]\T1/ptm/m/it/10 deployment_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: 
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_classes: Op-tion
al[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_time: Op-tional[date
time.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 bucket_size:
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[]\T1/ptm/m/it/10 deployment_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics: 
Op-

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\T1/ptm/m/it/10 tional[List[datarobot.enums.ACCURACY_METRIC]] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: Op-tional[str] =
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\T1/ptm/m/it/10 Op-tional[datetime.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
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[]

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[]\T1/ptm/m/it/10 role:

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\T1/ptm/m/it/10 user-

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\T1/ptm/m/it/10 name:

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\T1/ptm/m/it/10 **kwargs\T1/pcr/m/n/10 ) 
[291]
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[]

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: Op-
tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: 
int = 0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit: int = 100\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.external_dataset_scores_insights.external_
scores.ExternalScores] 

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n
/10 !$

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\T1/ptm/m/n/10 datarobot.models.external_dataset_scores_insights.external_score
s.ExternalScores 
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: int = 0\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 limit: int = 100\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.external_dataset_scores_insights.external_
lift_chart.ExternalLiftChart] 

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n
/10 !$

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chart.ExternalLiftChart 

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[]\T1/ptm/m/it/10 dataset_id: str, roc_points: List[EstimatedMetric],

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\T1/ptm/m/it/10 neg-a-tive_class_predictions: List[float], pos-i-
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: int = 0\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 limit: int = 100\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.external_dataset_scores_insights.external_
roc_curve.ExternalRocCurve] 

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n
/10 !$

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urve.ExternalRocCurve 

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=None\T1/ptm/m/n
/10 , \T1/ptm/m/it/10 name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_type=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 im-por-tance=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 low_informati
on=None\T1/ptm/m/n/10 ,

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 , \T1/ptm/m/it/10 mean=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 me-

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\T1/ptm/m/it/10 dian=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 std_dev=None\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 time_series_eligible=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 time_series_eligibility_reason=None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 time_step=None\T1/ptm/m/n/10 ,

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y=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-ti-l-
[294]
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[]\T1/ptm/m/n/10 {‘key’:’DataRobot’, ‘-sum-ma-ry’:{‘min’:0, ‘
-
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[]\T1/ptm/m/it/10 datetime_partition_column\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cro
ss_series_group_by_columns\T1/ptm/m/n/10 ,
[296] [297]
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\T1/ptm/m/it/10 im-por-tance=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 low_informati
on=None\T1/ptm/m/n/10 ,

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m/m/n/10 , \T1/ptm/m/it/10 max=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mean=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 me-dian=None\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 std_dev=None\T1/ptm/m/n/10 ,
[298]
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[]\T1/ptm/m/n/10 {‘key’:’DataRobot’, ‘-sum-ma-ry’:{‘min’:0, ‘
-

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[]\T1/ptm/m/it/10 id_\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 dataset_version_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_type=None\T1
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nt=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 std_dev=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_series_eligib
le=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 time_series_eligibility_reason=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 time_step=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_unit=None\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-
[299] [300] [301] [302]
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[]\T1/ptm/m/n/10 [list(dict)] List of cor-re-la-tion val-ues with a schema de-s
cribed as
[303]
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[]\T1/ptm/m/n/10 [list(dict)] List of joint prob-a-bil-ity val-ues with a schem
a de-scribed as

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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0,B=1) can be re-trieved via:

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\T1/pcr/m/n/10 pairwise_joint_probabilities.statistic_dataframes[(0,1)].

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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0,B=1) can be re-trieved via:

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\T1/pcr/m/n/10 pairwise_joint_probabilities.as_dataframe((0,1)).loc[\TS1/pcr/m/
n/10 '\T1/pcr/m/n/10 A\TS1/pcr/m/n/10 '\T1/pcr/m/n/10 ,
[304]
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[]\T1/ptm/m/n/10 The joint prob-a-bil-i-ties for the re-quested

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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0|B=1) can be re-trieved via:

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\T1/pcr/m/n/10 pairwise_conditional_probabilities.statistic_dataframes[(0,

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[]\T1/ptm/m/n/10 You might find it more con-ve-nient to use [][]\T1/pcr/m/sl/10
 Feature.get_pairwise_conditional_probabilities[][]
[305]
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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0|B=1) can be re-trieved via: \T1/
pcr/m/n/10 pairwise_conditional_probabilities.

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[]\T1/ptm/m/n/10 The con-di-tional prob-a-bil-i-ties for the re-quested

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[]\T1/ptm/m/it/10 strengths: Op-tional[List[Strength]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures: Op-

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\T1/ptm/m/it/10 tional[List[Feature]] = None\T1/ptm/m/n/10 ,
[306]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-ric: Op-t
ional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 as-so-ci-a-tion_type:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turel
ist_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.feature_association_matrix.feature_association_
matrix.FeatureAssociationMatrix 

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[]\T1/ptm/m/n/10 [enums.FEATURE_ASSOCIATION_METRIC] The name of a met-ric to

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[]\T1/ptm/m/n/10 [enums.FEATURE_ASSOCIATION_TYPE] The type of de-

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[]\T1/ptm/m/it/10 project_id: Op-tional[str]

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\T1/ptm/m/it/10 Op-tional[List[Tuple[Any\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tures: Op-tional[List[str]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 types: Op-

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\T1/ptm/m/it/10 tional[List[str]] = None\T1/ptm/m/n/10 ,
[307]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture1: st
r\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture2: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-turelist_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cm
sy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.feature_association_matrix.feature_association_
matrix_details.FeatureAssociationMatrixDetails 

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[]\T1/ptm/m/it/10 project_id: Op-tional[str]

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\T1/ptm/m/it/10 tional[List[FeatureListType]]

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[]\T1/ptm/m/it/10 project_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/
m/n/10 datarobot.models.feature_association_matrix.feature_association_featurel
ists.FeatureAssociationFeaturelists 

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_definitions=None\T
1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ture_discovery_mode=None\T1/ptm/m/n/10 ,
[308] [309]
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[]\T1/ptm/m/n/10 List of fea-ture deriva-tion win-

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\T1/ptm/m/n/10 dows set-tings. If present, time-aware joins will be used. Only 
al-lowed

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\T1/ptm/m/n/10 when fea-ture_derivation_window_start, fea-ture_derivation_windo
w_end and fea-

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[]\T1/ptm/m/n/10 Closest value of pre-dic-
[310]
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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.helpers.

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[]\T1/ptm/m/n/10 [list of fea-ture dis-cov-ery set-tings, op-tional] Each el-

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\T1/ptm/m/n/10 e-ment is a dic-tio-nary or a \T1/pcr/m/n/10 datarobot.helpers.f
eature_discovery.
[311]
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[]\T1/ptm/m/n/10 The re-quested re-la-tion-ships

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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.helpers.

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[]\T1/ptm/m/n/10 [list of fea-ture dis-cov-ery set-tings, op-tional] Each el-

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\T1/ptm/m/n/10 e-ment is a dic-tio-nary or a \T1/pcr/m/n/10 datarobot.helpers.f
eature_discovery.
[312]
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[]\T1/ptm/m/it/10 identifier: str,

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\T1/ptm/m/it/10 cat-a-log_id: Op-

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\T1/ptm/m/it/10 tional[str], cat-a-

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\T1/ptm/m/it/10 snap-shot_policy:

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\T1/ptm/m/it/10 str = 'lat-est',

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\T1/ptm/m/it/10 fea-ture_list_id:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 = None, pri-

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\T1/ptm/m/it/10 mary_temporal_key:

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\T1/ptm/m/it/10 Op-tional[str] =
[313]
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[]\T1/ptm/m/it/10 dataset2_identifier: str,

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\T1/ptm/m/it/10 dataset1_keys: List[str],

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\T1/ptm/m/it/10 dataset2_keys: List[str],

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\T1/ptm/m/it/10 dataset1_identifier: Op-

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\T1/ptm/m/it/10 tional[str] = None, fea-

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\T1/ptm/m/it/10 ture_derivation_window_start:

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\T1/ptm/m/it/10 ture_derivation_window_end:

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\T1/ptm/m/it/10 ture_derivation_window_time_unit:

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\T1/ptm/m/it/10 ture_derivation_windows:

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\T1/ptm/m/it/10 Op-tional[List[Dict[str,

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\T1/ptm/m/it/10 Union[int, str]]]]

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\T1/ptm/m/it/10 = None, pre-dic-

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\T1/ptm/m/it/10 tion_point_rounding_time_unit:
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[]\T1/ptm/m/n/10 Column(s) from the sec-ond

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[]\T1/ptm/m/n/10 List of fea-ture deriva-tion win-

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\T1/ptm/m/n/10 dows set-tings. If present, time-aware joins will be used. Only 
al-lowed

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\T1/ptm/m/n/10 when fea-ture_derivation_window_start, fea-ture_derivation_windo
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[]\T1/ptm/m/n/10 Closest value of pre-dic-
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[]\T1/ptm/m/it/10 id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id:

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\T1/ptm/m/it/10 str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-fig: Op-

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\T1/ptm/m/it/10 tional[List[DatasetConfiguration]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sec-

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\T1/ptm/m/it/10 ondary_datasets: Op-

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\T1/ptm/m/it/10 tional[List[SecondaryDataset]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-tial_ids:

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\T1/ptm/m/it/10 Op-tional[StoredCredentials]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_default: Op-

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\T1/ptm/m/it/10 tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_version: Op-

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[]\T1/ptm/m/n/10 List of sec-ondary datasets

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[]\T1/ptm/m/n/10 credentials used by the sec-ondary
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[]\T1/ptm/m/it/10 project_id: str, sec-ondary_datasets: List[datarobot.helpers.
feature_discovery.SecondaryDataset],

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\T1/ptm/m/it/10 name: str, fea-turelist_id: Op-tional[str] = None\T1/pcr/m/n/10
 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: 
Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/n/10 [list of Sec-ondary-Dataset-Con-fig-u-ra-tions] The re-

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\T1/ptm/m/it/10 log_version_id: str\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 ff_metadata_datetime_per_backtest\T1/pcr/m/n/10 ) 

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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_fit\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 back-
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[]\T1/ptm/m/it/10 ff_metadata_datetime_per_backtest\T1/pcr/m/n/10 ) 
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\T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures:

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\T1/ptm/m/it/10 List[str]\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/it/10 project_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/
m/n/10 datarobot.models.restore_discarded_features.DiscardedFeaturesInfo 
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[]\T1/ptm/m/it/10 data\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-pleted_resource_url=
None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.missing_r
eport.MissingValuesReport 

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m/m/n/10 , \T1/ptm/m/it/10 fea-turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 fea-

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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 n_clusters=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 has_empty_clust
ers=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-

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\T1/ptm/m/it/10 ports_monotonic_constraints=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tio
n_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 ,
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[]
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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
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\T1/ptm/m/it/10 score=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasin
g_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
odel_number=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 train-

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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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\T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-

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\T1/ptm/m/it/10 ple_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_coun
t=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-

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\T1/ptm/m/it/10 sam-pling_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_typ
e=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_info=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_score=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_status=
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data_selection_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back
-tests=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 ef-fec-

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\T1/ptm/m/it/10 tive_feature_derivation_window_start=None\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 ef-

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\T1/ptm/m/it/10 fec-tive_feature_derivation_window_end=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fore-cast_window_start=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 for
e-

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ports_composable_ml=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 n_clus
ters=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_n_clusters_dynamically_determined=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [str or None] (New in v2.23) in-di-cates the way train-ing dat
a
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[]\T1/ptm/m/it/10 offset=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=100\T1/ptm/m/n
/10 , \T1/ptm/m/it/10 met-ric=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-ti-
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[]\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 f
ea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1
/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-i
ng_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_end_date=None\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 sam-

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/it/10 backtest_index\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None
\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest_index\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source\T1/ptm/
m/n/10 ,

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\T1/ptm/m/it/10 top_n_features=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_=None\T1/p
tm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:

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\T1/ptm/m/it/10 'val-i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_dist
ance_start=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-
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[]\T1/ptm/m/n/10 [AccuracyOverTimePlotsMetadata] a [][]\T1/pcr/m/sl/10 Accuracy
OverTimePlotsMetadata[][]

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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION: 'val-

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\T1/ptm/m/it/10 i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_distance=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 se-ries_id=None\T1/ptm/m/n/10 , \T1/ptm/m/
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e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_date=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:

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\T1/ptm/m/it/10 'val-i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_dist
ance=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 se-
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:

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\T1/ptm/m/it/10 'val-i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_dist
ance_start=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-

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\T1/ptm/m/it/10 max_bin_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_date=No
ne\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_date=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:
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[]\T1/ptm/m/it/10 prediction_threshold=0.5\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back
-test=0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 source=<SOURCE_TYPE.VALIDATION: 'val-i-
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from

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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-

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\T1/ptm/m/it/10 rics=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model_id=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/
it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
odel_number=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_name: Op-tional[st
r] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 note: Op-

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 im-ported_by_username: Op-
tional[str] =

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\T1/ptm/m/it/10 ated_by_username: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 cre-
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[]\T1/ptm/m/it/10 path: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: int = 600
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 limit: Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
off-set: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro-cesses=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist
_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_du
ration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-

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\T1/ptm/m/it/10 ing_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_type=No
ne\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 rat-ing_table_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/
it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 train-

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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the

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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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[]\T1/ptm/m/n/10 [dict] “-Pa-ram-e-ter-s” dic-tio-nary, same as spec-i-fied
 on

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[]

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_job_id: str\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 eureqa_solution_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-plex-i
ty\T1/ptm/m/n/10 , \T1/ptm/m/it/10 er-

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\T1/ptm/m/it/10 ror\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pres-sion\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 ex-pres-sion_annotated\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 datetime_partition_column: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 au-topi-lot_data_selection_method:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 val-i-da-


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\T1/ptm/m/it/10 tion_duration: Op-tional[str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_start_date=None\T
1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_duration: Op-tional[str]

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num-

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\T1/ptm/m/it/10 ber_of_backtests: Op-tional[int]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back-tests=None\T1/ptm/m
/n/10 ,

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\T1/ptm/m/it/10 bool = False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_derivation_window_start:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_derivation_window_end:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 win-

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\T1/ptm/m/it/10 dows_basis_unit: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 treat_as_exponential:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dif-fer-

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\T1/ptm/m/it/10 enc-ing_method: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pe-ri-od-ic-i-ties=None\
T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mul-ti-series_id_columns: Op-

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\T1/ptm/m/it/10 tional[List[str]] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_cross_series_features: Op-

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\T1/ptm/m/it/10 tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ag-gre-ga-

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\T1/ptm/m/it/10 cross_series_group_by_columns:

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\T1/ptm/m/it/10 Op-tional[List[str]] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cal-en-dar_id: Op-tional[str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_end_date=None\T1/
ptm/m/n/10 ,

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\T1/ptm/m/it/10 un-su-per-vised_mode: bool

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\T1/ptm/m/it/10 = False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_splits: Op-

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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 low_partial_history_time_series_predictions:
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[]\T1/ptm/m/n/10 This is the spec-i-fi-ca-tion that should be passed to [][]\T1
/pcr/m/sl/10 Project.analyze_and_model[][] \T1/ptm/m/n/10 via the

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the

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[]\T1/ptm/m/n/10 Note that ei-ther (\T1/pcr/m/n/10 holdout_start_date\T1/ptm/m/
n/10 , \T1/pcr/m/n/10 holdout_duration\T1/ptm/m/n/10 ) or (\T1/pcr/m/n/10 holdo
ut_start_date\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-

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[]\T1/ptm/m/n/10 [datetime.datetime or None] The start date of hold-out scor-in
g

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[]\T1/ptm/m/n/10 [str or None] The du-ra-tion of the hold-out scor-ing data. If

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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of

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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)
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\T1/ptm/m/it/10 val-i-da-tion_end_date: Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri-mary_training_start_
date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pri-mary_training_end_date: Op-

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\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string s
uch as those re-turned by the

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[]\T1/ptm/m/n/10 Option 2 (New in ver-sion v2.20): Use \T1/pcr/m/n/10 index\T1/
ptm/m/n/10 , \T1/pcr/m/n/10 primary_training_start_date\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 , and \T1/pcr/m/n/10 validation_end_date\T1/ptm/m/
n/10 .
[479]
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[]\T1/ptm/m/n/10 [bool][Switch to us-ing the older a_priori key name in-stead o
f
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[]\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date-time_par
titioning_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 au-topi-lot_data_selection_method=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ava
il-

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\T1/ptm/m/it/10 able_training_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 a
vail-

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\T1/ptm/m/it/10 able_training_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ava
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\T1/ptm/m/it/10 able_training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 av
ail-

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\T1/ptm/m/it/10 able_training_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
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\T1/ptm/m/it/10 mary_training_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 p
ri-

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\T1/ptm/m/it/10 mary_training_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
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\T1/ptm/m/it/10 mary_training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pr
i-

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\T1/ptm/m/it/10 gap_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_row_count
=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dis-able_hold
out=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_du
ration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-ou
t_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_backtests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back-
tests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 to-

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\T1/ptm/m/it/10 tal_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use_time_ser
ies=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 de-fault_to_known_in_advance=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 de-fault_to_do_not_derive=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 fea-

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\T1/ptm/m/it/10 ture_derivation_window_start=None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 fea-

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\T1/ptm/m/it/10 ture_derivation_window_end=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 fea-

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\T1/ptm/m/it/10 ture_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_wi
ndow_start=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fore-cast_window_end=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 win-

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\T1/ptm/m/it/10 dows_basis_unit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 treat_as_e
xponential=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dif-fer-enc-ing_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pe-
ri-od-ic-i-

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\T1/ptm/m/it/10 ties=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-ti-series_id_colu
mns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_known_in_advance_features=0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_do_not_derive_features=0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_cross_series_features=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ag-gre-ga-tion_type=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cross_series_group_by_columns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cal-en-dar_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-en-

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\T1/ptm/m/it/10 dar_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_splits=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 low_partial_history_time_series_predictions=False\T1/ptm/m/n/10
 ,
[481] [482]
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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-ated
[483]
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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of
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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 spec: datarob
ot.helpers.partitioning_methods.DatetimePartitioningSpecification\T1/ptm/m/n/10
 ,
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[]\T1/ptm/m/it/10 use_holdout_start_end_format: bool = False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_backtest_start_end_format: bool = False\T1/pcr/m/n/10 ) $\O
MS/cmsy/m/n/10 !$

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 index: Op-tional[int]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 avail-

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\T1/ptm/m/it/10 able_training_start_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 avail-

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\T1/ptm/m/it/10 able_training_duration:

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\T1/ptm/m/it/10 able_training_row_count:

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\T1/ptm/m/it/10 able_training_end_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri-

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\T1/ptm/m/it/10 mary_training_start_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri-

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\T1/ptm/m/it/10 mary_training_duration:

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\T1/ptm/m/it/10 mary_training_row_count:

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\T1/ptm/m/it/10 mary_training_end_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_start_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_duration:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_row_count: Op-

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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime.datetime] =

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_row_count:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 to-tal_row_count:
[488] [489]
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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 validation_duration\T1/ptm/m/n/10 ). If \T1/p
cr/m/n/10 True\T1/ptm/m/n/10 , will use a start/end date ap-

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\T1/ptm/m/n/10 proach for spec-i-fy-ing back-tests (\T1/pcr/m/n/10 primary_trai
ning_start_date\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 ,
[490]
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\T1/ptm/m/it/10 0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 months:

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\T1/ptm/m/it/10 int = 0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 min-utes:

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\T1/ptm/m/it/10 true_negative_value: Op-tional[float] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 false_positive_value: Op-tional[float] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 id: str\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 true_posit
ive_value: float\T1/ptm/m/n/10 ,
[493]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dict_job_id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait: int

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\T1/ptm/m/it/10 = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 pan-

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[]\T1/ptm/m/it/10 job: datarobot.models.job.Job\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/
10 !$ \T1/ptm/m/n/10 datarobot.models.predict_job.PredictJob 
[494]
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[]\T1/ptm/m/n/10 If the generic Job was not a pre-dict job, e.g. job_type !=
[495] [496]
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\T1/ptm/m/it/10 pre-dic-tions_end_date: Op-tional[datetime] =

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\T1/ptm/m/it/10 tional[List[DetectedActualValueColumn]] =
[497]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: s
tr\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.predict
ion_dataset.PredictionDataset 
[498]
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\T1/ptm/m/it/10 tion_explanations_sample=None\T1/pcr/m/n/10 ) 

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n
/10 ,

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\T1/ptm/m/it/10 max_explanations\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num_columns\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fin-

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\T1/ptm/m/it/10 ish_time\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_explanati
ons_location\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 thresh-old_low=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 thresh-old_
high=None\T1/ptm/m/n/10 ,
[499]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_explanati
ons=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 thresh-

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[]\T1/ptm/m/n/10 Compute a Pre-dic-tion-Ex-pla-na-tion-sIni-tial-iza-tion for t
he model via \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Compute pre-dic-tions for the model and dataset via \T1/pcr/m/
n/10 datarobot.Model.
[500] [501] [502]
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[]\T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 ex-

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[]\T1/ptm/m/it/10 row_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion_values\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion_explanations=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ad-

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\T1/ptm/m/it/10 justed_prediction=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 justed_prediction_values=None\T1/pcr/m/n/10 ) 
[503]
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[]\T1/ptm/m/n/10 [list] an ar-ray of dic-tio-nar-ies with a schema de-scribed a
s

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 count=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 vi-

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\T1/ptm/m/it/10 ous=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 next=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion_explanations_record_location=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ment_method=None\T1/pcr/m/n/10 ) 
[504]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_expla
nations_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 off-set=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-
[505] [506]
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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: 
Op-tional[str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-cludes_prediction_inter
vals: Op-tional[bool]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_intervals_s
ize: Op-tional[int]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point: Op-tion
al[datetime] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_start_date: 
Op-tional[datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_end_date: 
Op-tional[datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_value_column: Op
-tional[str] =
[507] [508] [509] [510] [511]
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[]\T1/ptm/m/n/10 Raised if an ob-ject passed to the \T1/pcr/m/n/10 options \T1/
ptm/m/n/10 pa-ram-e-ter is not

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\T1/ptm/m/n/10 an \T1/pcr/m/n/10 AdvancedOptions \T1/ptm/m/n/10 in-stance, a va
lid key-word ar-gu-ment from the
[512]
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[]\T1/ptm/m/it/10 sourcedata\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_name='Unti
tled Project'\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 ,
[513] [514] [515]
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[]\T1/ptm/m/it/10 clustering_project_id:

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\T1/ptm/m/it/10 str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 clus-ter-

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\T1/ptm/m/it/10 ing_model_id:

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\T1/ptm/m/it/10 max_wait: int

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\T1/ptm/m/it/10 = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 sourcedata: Union[str, pan-das.core.frame.DataFrame], tar-get
: Op-

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\T1/ptm/m/it/10 Op-tional[int] = None, met-ric: Op-tional[str] = None, au-topi-


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\T1/ptm/m/it/10 lot_on: bool = True, blueprint_threshold: Op-tional[int] = None
,

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\T1/ptm/m/it/10 re-sponse_cap: Op-tional[float] = None, par-ti-tion-ing_method:
 Op-

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\T1/ptm/m/it/10 tional[datarobot.helpers.partitioning_methods.PartitioningMetho
d] =

Underfull \hbox (badness 10000) in paragraph at lines 43901--43901
\T1/ptm/m/it/10 None, pos-i-tive_class: Union[int, float, str, None] = None, ta
r-

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\T1/ptm/m/it/10 Op-tional[bool] = None, con-sider_blenders_in_recommendation: O
p-

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\T1/ptm/m/it/10 tional[bool] = None, scor-ing_code_only: Op-tional[bool] = None
,

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\T1/ptm/m/it/10 min_secondary_validation_model_count: Op-tional[int] = None,

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\T1/ptm/m/it/10 Op-tional[str] = None, au-topi-lot_with_feature_discovery: Op-

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\T1/ptm/m/it/10 tional[bool] = None, fea-ture_discovery_supervised_feature_redu
ction:

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\T1/ptm/m/it/10 Op-tional[bool] = None, un-su-per-vised_type: Op-

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\T1/ptm/m/it/10 tional[datarobot.enums.UnsupervisedTypeEnum] = None, au-topi-
[516] [517]
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[]\T1/ptm/m/n/10 Compute “All back-test”

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[]\T1/ptm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[518]
Underfull \hbox (badness 10000) in paragraph at lines 44074--44074
[]\T1/ptm/m/it/10 search_params: Op-tional[Dict[str\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 str]] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/n/10 Project.list(search_params={‘project_name’: ‘red’}) >>
>
[519]
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[]\T1/ptm/m/it/10 target=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mode='quick'\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 worker
_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-

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\T1/ptm/m/it/10 i-tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ti-tion-i
ng_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/
10 ,

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\T1/ptm/m/it/10 ad-vanced_options=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait
=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 cre-

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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_enginee
ring_prediction_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 un-

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\T1/ptm/m/it/10 su-per-vised_mode=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-la-t
ion-ships_configuration_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 class_mapping_aggregation_settings=None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 seg-men-ta-tion_task_id=None\T1/ptm/m/n/10 ,
[520]
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 advanced_options\T1/ptm/m/n/10 , \T1/
pcr/m/n/10 partitioning_method \T1/ptm/m/n/10 or

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[]\T1/ptm/m/it/10 target=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mode='quick'\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 worker
_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i-

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\T1/ptm/m/it/10 tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ti-tion-ing
_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10
 , \T1/ptm/m/it/10 ad-

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\T1/ptm/m/it/10 vanced_options=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=60
0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 cre-den-tials=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-ture_engineering_prediction_point=None\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 un-su-per-vised_mode=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-la-ti
on-
[521] [522]
Underfull \hbox (badness 10000) in paragraph at lines 44436--44439
[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 advanced_options\T1/ptm/m/n/10 , \T1/
pcr/m/n/10 partitioning_method \T1/ptm/m/n/10 or

Underfull \hbox (badness 10000) in paragraph at lines 44463--44463
[]\T1/ptm/m/it/10 order_by: Union[str, List[str], None] = None, search_params: 
Op-

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\T1/ptm/m/it/10 tional[Dict[str, Any]] = None, with_metric: Op-tional[str] = No
ne\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[523] [524] [525]
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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_filename=None\T1/ptm
/m/n/10 ,

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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 cre-den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-
[526]
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 forecast_point \T1/ptm/m/n/10 or \T1/
pcr/m/n/10 predictions_start_date \T1/ptm/m/n/10 and

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[]\T1/ptm/m/it/10 data_source_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user-name\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 pass-word\T1/ptm/m/n/10 ,

Underfull \hbox (badness 10000) in paragraph at lines 44795--44795
\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
ne\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/it/10 lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1/pt
m/m/it/10 cre-

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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 ,
[527]
Underfull \hbox (badness 10000) in paragraph at lines 44861--44861
[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_version_id
=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
ne\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/it/10 lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1/pt
m/m/it/10 cre-

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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sec-
[528]
Underfull \hbox (badness 10000) in paragraph at lines 44956--44956
[]\T1/ptm/m/it/10 batch_size: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$
[529]
Underfull \hbox (badness 10000) in paragraph at lines 45098--45098
[]\T1/ptm/m/it/10 batch_size: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

Overfull \hbox (7.46193pt too wide) in paragraph at lines 45128--45128
[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.restore
_discarded_features.DiscardedFeaturesInfo 
[530]
Underfull \hbox (badness 10000) in paragraph at lines 45143--45143
[]\T1/ptm/m/it/10 features: List[str], max_wait: int = 600\T1/pcr/m/n/10 ) $\OM
S/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.restore_discarded_features.FeatureRestorationSt
atus 

Underfull \hbox (badness 6526) in paragraph at lines 45160--45160
\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait:
 int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[531]
Underfull \hbox (badness 10000) in paragraph at lines 45290--45290
\T1/ptm/m/it/10 start-ing_featurelist: Op-tional[datarobot.models.featurelist.F
eaturelist]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start-ing_featurelist_id
: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start-
[532]
Underfull \hbox (badness 10000) in paragraph at lines 45381--45381
[]\T1/ptm/m/it/10 name: str, fea-tures: List[str],

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\T1/ptm/m/it/10 skip_datetime_partition_column: bool = False\T1/pcr/m/n/10 ) $\
OMS/cmsy/m/n/10 !$
[533] [534]
Underfull \hbox (badness 10000) in paragraph at lines 45544--45544
\T1/ptm/m/it/10 bool = True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-sider_blenders_
in_recommendation: bool = False\T1/ptm/m/n/10 ,
[535]
Underfull \hbox (badness 10000) in paragraph at lines 45614--45614
[]\T1/ptm/m/it/10 trainable\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it
/10 source_project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-

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\T1/ptm/m/it/10 ing_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_cou
nt=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=<o
bject
[536]
Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
[]\T1/ptm/m/it/10 blueprint_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 ,

Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
\T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source_
project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
\T1/ptm/m/it/10 tonic_increasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 mono-

Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 use_project_settings=False\T1/ptm/m/n/10 ,

Underfull \hbox (badness 6493) in paragraph at lines 45737--45741
[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the
[537]
Underfull \hbox (badness 5260) in paragraph at lines 45750--45753
\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor

Underfull \hbox (badness 10000) in paragraph at lines 45817--45820
[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.
[538]
Underfull \hbox (badness 10000) in paragraph at lines 45847--45847
[]\T1/ptm/m/it/10 model_ids: List[str], blender_method: str\T1/pcr/m/n/10 ) $\O
MS/cmsy/m/n/10 !$

Underfull \hbox (badness 10000) in paragraph at lines 45855--45858
[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.

Underfull \hbox (badness 10000) in paragraph at lines 45903--45906
[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
[539]
Underfull \hbox (badness 10000) in paragraph at lines 46021--46021
[]\T1/ptm/m/it/10 status: Op-tional[datarobot.enums.Enum] = None\T1/pcr/m/n/10 
) $\OMS/cmsy/m/n/10 !$

Underfull \hbox (badness 10000) in paragraph at lines 46025--46028
[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

Underfull \hbox (badness 10000) in paragraph at lines 46053--46053
[]\T1/ptm/m/it/10 status: Op-tional[datarobot.enums.Enum] = None\T1/pcr/m/n/10 
) $\OMS/cmsy/m/n/10 !$

Underfull \hbox (badness 10000) in paragraph at lines 46057--46060
[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
[540]
Underfull \hbox (badness 10000) in paragraph at lines 46099--46104
[]\T1/ptm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
[541]
Underfull \hbox (badness 10000) in paragraph at lines 46193--46193
[]\T1/ptm/m/it/10 advanced_options: Op-tional[datarobot.helpers.AdvancedOptions
] =
[542]
Underfull \hbox (badness 10000) in paragraph at lines 46295--46300
[]\T1/ptm/m/n/10 (New in ver-sion v2.23) one of

Underfull \hbox (badness 10000) in paragraph at lines 46295--46300
\T1/pcr/m/n/10 datarobot.enums.DATETIME_AUTOPILOT_DATA_SAMPLING_METHOD\T1/ptm/m
/n/10 .

Underfull \hbox (badness 10000) in paragraph at lines 46324--46328
[]\T1/ptm/m/n/10 Time Se-ries only.
[543]
Underfull \hbox (badness 10000) in paragraph at lines 46343--46349
\T1/pcr/m/n/10 predictionBalance\T1/ptm/m/n/10 , \T1/pcr/m/n/10 trueFavorableAn
dUnfavorableRateParity \T1/ptm/m/n/10 or

Overfull \hbox (4.03096pt too wide) in paragraph at lines 46359--46366
[]\T1/ptm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e

Underfull \hbox (badness 10000) in paragraph at lines 46396--46396
[]\T1/ptm/m/it/10 cv_method: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it
/10 val-i-da-tion_type: Op-

Underfull \hbox (badness 10000) in paragraph at lines 46396--46396
\T1/ptm/m/it/10 tional[datarobot.helpers.partitioning_methods.PartitioningMetho
d]
[544]
Underfull \hbox (badness 5022) in paragraph at lines 46453--46456
[]\T1/ptm/m/n/10 [PartitioningMethod, op-tional] An in-stance of \T1/pcr/m/n/10
 datarobot.
[545] [546] [547]
Underfull \hbox (badness 10000) in paragraph at lines 46687--46689
[]\T1/ptm/m/n/10 If value of \T1/pcr/m/n/10 variable_type \T1/ptm/m/n/10 is not
 from \T1/pcr/m/n/10 datarobot.enums.

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[]\T1/ptm/m/it/10 new_project_name: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.relatio
nships_configuration.RelationshipsConfiguration 
[548]
Underfull \hbox (badness 10000) in paragraph at lines 46871--46871
[]\T1/ptm/m/it/10 catalog_version_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-

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\T1/ptm/m/it/10 get: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date-time_partitioning
:

Overfull \hbox (60.46223pt too wide) in paragraph at lines 46871--46871
\T1/ptm/m/it/10 datarobot.helpers.partitioning_methods.DatetimePartitioning\T1/
ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

Overfull \hbox (126.07123pt too wide) in paragraph at lines 46871--46871
\T1/ptm/m/n/10 datarobot.models.external_baseline_validation.ExternalBaselineVa
lidationInfo 
[549] [550]
Underfull \hbox (badness 10000) in paragraph at lines 47033--47033
[]\T1/ptm/m/it/10 bias_mitigation_parent_leaderboard_id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 in-clude_bias_mitigation_feature_as_predictor_variable: bool\T1
/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e

Underfull \hbox (badness 10000) in paragraph at lines 47077--47077
[]\T1/ptm/m/it/10 bias_mitigation_feature_name: str\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.model.BiasMitigationFeatureInfo 

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[]\T1/ptm/m/it/10 bias_mitigation_feature_name: str\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$
[551]
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[]\T1/ptm/m/it/10 datetime_partition_spec: Op-tional[datarobot.helpers.partitio
ning_methods.DatetimePartitioningSpecification]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 **kwargs\T1/pcr/m/n/10 )
 $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.helpers.partitioning_methods.Dat
etimePartitioning 
[552]
Overfull \hbox (108.37086pt too wide) in paragraph at lines 47226--47226
[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Op-tional[datarobot.help
ers.partitioning_methods.DatetimePartitioningSpecification] 

Underfull \hbox (badness 6461) in paragraph at lines 47251--47251
[]\T1/ptm/m/it/10 supported: bool\T1/ptm/m/n/10 ,
[553]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 rat-ing_table
_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 project_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/
m/n/10 List[datarobot.models.recommended_model.ModelRecommendation] 

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[]\T1/ptm/m/it/10 recommended_models: List[datarobot.models.recommended_model.M
odelRecommendation],

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\T1/ptm/m/it/10 rec-om-men-da-tion_type: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 
!$ \T1/ptm/m/n/10 Op-

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[]\T1/ptm/m/it/10 id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro
ject_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seg-men-ta-tion_task_id:
 Op-tional[str] = None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-bined_mod
el_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_duration: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 train-

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\T1/ptm/m/it/10 ing_start_date: Op-tional[datetime.datetime] = None\T1/ptm/m/n/
10 ,
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\T1/ptm/m/it/10 Op-tional[List[str]] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use
r_defined_segment_id_columns: Op-

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seg-men-ta-ti
on_task_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 List[datarobot.models.segm
entation.SegmentInfo] 

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\T1/ptm/m/it/10 Op-tional[List[str]] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use
r_defined_segment_id_columns: Op-
[562]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seg-men-ta-ti
on_task_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/n/10 Currently [][]\T1/pcr/m/sl/10 DataStores[][]\T1/ptm/m/n/10 , [
][]\T1/pcr/m/sl/10 DataSources[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 Datasets
[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 Projects[][] \T1/ptm/m/n/10 (new in ve
r-sion v2.15) and

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[564]
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[]\T1/ptm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
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[566] [567] [568]
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[]

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[]

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[570]
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[]\T1/ptm/m/it/10 user_blueprint_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 projec
t_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 blueprint\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_type: Op-tion
al[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 save_to_c
atalog: bool = True\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/it/10 tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprint 
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[]\T1/ptm/m/it/10 blueprint_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id:
 str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 save_to_catalog: bool = True\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 
!$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprint 

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[]\T1/ptm/m/it/10 user_blueprint_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_
type: Op-

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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id: 
Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 save_to_catalog: bool = 
True\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.user_bl
ueprints.models.UserBlueprintAvailableInput 

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tMenu 
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[]\T1/ptm/m/it/10 project_id: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 user_blueprint_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprintAvailableTa
sks 

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[]\T1/ptm/m/it/10 output_method: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 task_code:


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\T1/ptm/m/it/10 str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 task_parameters\T1/ptm/m/n/
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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kParameters 

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[]\T1/ptm/m/n/10 A

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RolesResponseValidator] 
[574]
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[]\T1/ptm/m/it/10 blueprint\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id: Op-tion
al[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.user_blueprints.models.VertexContextItem] 


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[]\T1/ptm/m/it/10 user_blueprint_id: str, roles:

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ontrolWithUsernameValidator,

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dValidator]]\T1/pcr/m/n/10 )

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[]
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[]\T1/ptm/m/it/10 search: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 tag: Op-tional[str] =

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 owner_use
rname: Op-

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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 or-der_by: s
tr = '-created'\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 project_id: str, fea-ture_name: str, tar-get_value: Union[str
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\T1/ptm/m/it/10 float, List[str], None] = None, tar-get_bin_start: Union[float,
 int,

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\T1/ptm/m/it/10 None] = None, tar-get_bin_end: Union[float, int, None] = None,

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\T1/ptm/m/it/10 off-set: Op-tional[int] = None, limit: Op-tional[int] = None\T1
/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 image_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count: int\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_name: str\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_name: str\T1/ptm/m/n/10 , \T1/ptm/m/i
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t: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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on]]
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\T1/ptm/m/it/10 tual_target_value:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[Union[str,

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\T1/ptm/m/it/10 tion:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[PredictionType]

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\T1/ptm/m/it/10 =

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\T1/ptm/m/it/10 None\T1/pcr/m/n/10 ) 
[596] [597]
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[]\T1/ptm/m/it/10 model_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_name: 
str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 doc-u-ment_index: Op-

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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 doc-u-ment_t
ask: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot._experimental.models.documentai.document.Document
TextExtractionSamplePage] 

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[]\T1/ptm/m/it/10 id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 Op-tional[str]

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[]\T1/ptm/m/it/10 deployment_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/p
tm/m/n/10 List[datarobot._experimental.models.retraining.RetrainingPolicy] 
[598] [599]
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[]\T1/ptm/m/it/10 deployment_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-train-i
ng_policy_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[600]
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[]\T1/ptm/m/it/10 project_id:

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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: in
t = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 pan-
[601]
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[]\T1/ptm/m/it/10 featurelist_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 query_fil
e_path: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num-
[602]
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[]\T1/ptm/m/it/10 data_matching_id:

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\T1/ptm/m/it/10 **kwargs\T1/pcr/m/n/10 ) 

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[]\T1/ptm/m/it/10 project_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/
m/n/10 List[datarobot._experimental.models.data_matching.DataMatchingQuery] 

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\T1/ptm/m/it/10 Any], model:

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\T1/ptm/m/it/10 tional[Dict[str,

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\T1/ptm/m/it/10 Any]] =

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[]\T1/ptm/m/it/10 model_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-per-i-ment_c
ontainer_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 Retrieve lin-eage in-for-ma-tion about a trained model. If you p
ass the op-tional
[603] [604]
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[]

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[]\T1/ptm/m/n/10 Added new meth-ods [][]\T1/pcr/m/sl/10 DataEngineQueryGenerato
r.prepare_prediction_dataset[][] \T1/ptm/m/n/10 and

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[][]\T1/pcr/m/sl/10 DataEngineQueryGenerator.prepare_prediction_dataset_from_ca
talog[][] \T1/ptm/m/n/10 that ap-ply
[605]
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[]\T1/ptm/m/n/10 Update the doc-u-men-ta-tion to sug-gest that set-ting \T1/ptm
/m/it/10 use_backtest_start_end_format \T1/ptm/m/n/10 of
[606]
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[]\T1/ptm/m/n/10 Deprecated method [][]\T1/pcr/m/sl/10 Project.open_leaderboard
_browser[][]\T1/ptm/m/n/10 . Please use [][]\T1/pcr/m/sl/10 Project.
[607]
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[]\T1/ptm/m/n/10 Added new con-ve-nience meth-ods to [][]\T1/pcr/m/sl/10 dataro
bot.models.Project[][] \T1/ptm/m/n/10 for deal-ing with par-ti-tion
[608]
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[]\T1/ptm/m/n/10 Added sup-port for pass-ing in a cre-den-tials ID or cre-den-t
ials data to [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added two new prop-er-ties to [][]\T1/pcr/m/sl/10 datarobot.mo
dels.Project[][]\T1/ptm/m/n/10 : - \T1/pcr/m/n/10 catalog_id \T1/ptm/m/n/10 -

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[]\T1/ptm/m/n/10 Added sup-port for cre-at-ing mono-tonic fea-ture lists for ti
me se-ries projects. Set

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[]\T1/ptm/m/n/10 Added in-for-ma-tion about ver-tex to ad-vanced tun-ing pa-ram
-e-ters [][]\T1/pcr/m/sl/10 datarobot.models.Model.

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[]\T1/ptm/m/n/10 Fixed is-sue with por-tions of the pay-load not us-ing camel-C
as-ing in [][]\T1/pcr/m/sl/10 Project.
[609]
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[][][]\T1/pcr/m/sl/10 CustomModelVersion[][]\T1/ptm/m/n/10 ’s \T1/pcr/m/n/10 
required_metadata \T1/ptm/m/n/10 pa-ram-e-ter has been re-moved. Use

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[][][]\T1/pcr/m/sl/10 CustomTaskVersion[][]\T1/ptm/m/n/10 ’s \T1/pcr/m/n/10 r
equired_metadata \T1/ptm/m/n/10 pa-ram-e-ter has been re-moved. Use

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[]\T1/ptm/m/n/10 The class \T1/pcr/m/n/10 CustomInferenceImage \T1/ptm/m/n/10 w
as re-moved. Use [][]\T1/pcr/m/sl/10 CustomModelVersion[][] \T1/ptm/m/n/10 with

[610]
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[]\T1/ptm/m/n/10 Added ex-per-i-men-tal sup-port to re-trieve doc-u-ment text e
x-trac-tion sam-ples us-ing: -

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[][]\T1/pcr/m/sl/10 DocumentTextExtractionSample[][] \T1/ptm/m/n/10 - [][]\T1/p
cr/m/sl/10 DocumentTextExtractionSamplePage[][] \T1/ptm/m/n/10 -

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[]\T1/ptm/m/n/10 Added sup-port to pass cal-cu-la-tion mode to pre-dic-tion ex-
pla-na-tions (\T1/ptm/m/it/10 mode \T1/ptm/m/n/10 pa-ram-e-ter in

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[][]\T1/pcr/m/sl/10 PredictionExplanations.create[][]\T1/ptm/m/n/10 ) as well a
s batch scor-ing (\T1/ptm/m/it/10 ex-pla-na-tions_mode \T1/ptm/m/n/10 in
[611]
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[]\T1/ptm/m/n/10 Added sup-port for bias mit-i-ga-tion with the fol-low-ing met
h-ods - [][]\T1/pcr/m/sl/10 Project.

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\T1/pcr/m/sl/10 get_bias_mitigated_models[][] \T1/ptm/m/n/10 - [][]\T1/pcr/m/sl
/10 Project.apply_bias_mitigation[][]

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\T1/ptm/m/n/10 - [][]\T1/pcr/m/sl/10 Project.request_bias_mitigation_feature_in
fo[][] \T1/ptm/m/n/10 - [][]\T1/pcr/m/sl/10 Project.

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\T1/pcr/m/sl/10 get_bias_mitigation_feature_info[][] \T1/ptm/m/n/10 and by addi
ng new bias mit-i-ga-

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\T1/ptm/m/n/10 tion params - bias_mitigation_feature_name - bias_mitigation_tec
hnique - in-
[612]
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[]\T1/ptm/m/n/10 Update the doc-u-men-ta-tion to sug-gest that \T1/ptm/m/it/10 
fea-ture_derivation_window_end \T1/ptm/m/n/10 of [][]\T1/pcr/m/sl/10 datarobot.


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[]\T1/ptm/m/n/10 Added new pa-ram-e-ter \T1/ptm/m/it/10 up-load_read_timeout \T
1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 BatchPredictionJob.score[][] \T1/ptm/m/n/10
 and

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[]\T1/ptm/m/n/10 Added the abil-ity to turn off su-per-vised fea-ture re-duc-ti
on for Time Se-ries projects. Op-tion
[613] [614]
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[]

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[]\T1/ptm/m/n/10 Added the abil-ity to re-trieve de-tected er-rors in the po-te
n-tially mul-ti-cat-e-gor-i-cal fea-

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\T1/ptm/m/n/10 ture types that pre-vented the fea-ture to be iden-ti-fied as mu
l-ti-cat-e-gor-i-cal. [][]\T1/pcr/m/sl/10 Project.
[615]
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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.

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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.

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[]\T1/ptm/m/n/10 Added the abil-ity to con-trol class map-ping ag-gre-ga-tion i
n mul-ti-class projects via

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[]\T1/ptm/m/n/10 Added the abil-ity to com-pute and re-trieve Fea-ture Ef-fects
 for a Mul-ti-class model us-ing

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[][]\T1/pcr/m/sl/10 datarobot.models.Model.request_feature_effects_multiclass()
[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.Model.get_feature_effects_multiclass()[][] \T1/ptm/m/n/1
0 or [][]\T1/pcr/m/sl/10 datarobot.models.Model.

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[][]\T1/pcr/m/sl/10 datarobot.models.DatetimeModel.get_feature_effects_multicla
ss()[][] \T1/ptm/m/n/10 or

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[][]\T1/pcr/m/sl/10 datarobot.models.DatetimeModel.get_or_request_feature_effec
ts_multiclass()[][]
[616]
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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.get_predictions_by_forecast_d
ate_settings()[][] 

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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.update_predictions_by_forecas
t_date_settings()[][] 
[617]
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[]\T1/ptm/m/n/10 Added the abil-ity to re-quest a spe-cific model be pre-pared 
for de-ploy-ment us-ing [][]\T1/pcr/m/sl/10 Project.
[618] [619]
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[]\T1/ptm/m/n/10 Try to read Fea-ture Im-pact re-sult if ex-ist-ing jo-bId is N
one in [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Updated [][]\T1/pcr/m/sl/10 project.start_autopilot[][] \T1/pt
m/m/n/10 to ac-cept the fol-low-ing new GA pa-ram-e-ters

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\T1/ptm/m/n/10 that are al-ready in the pub-lic API: \T1/pcr/m/n/10 consider_bl
enders_in_recommendation\T1/ptm/m/n/10 ,
[620] [621]
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[]\T1/ptm/m/n/10 Added new helper func-tion to projects to re-trieve the rec-om
-mended model. [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 docker_context_size \T1/ptm/m/n/10 and \T
1/pcr/m/n/10 docker_image_size \T1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 datarobot.


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[]\T1/ptm/m/n/10 Arguments \T1/pcr/m/n/10 dataset_definitions \T1/ptm/m/n/10 an
d \T1/pcr/m/n/10 relationships \T1/ptm/m/n/10 now ac-cept [][]\T1/pcr/m/sl/10 D
atasetDefinition[][]
[622]
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\T1/pcr/m/n/10 allow_partial_history_time_series_predictions \T1/ptm/m/n/10 at-
tribute of the [][]\T1/pcr/m/sl/10 datarobot.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve per-class lift chart data for mul-ti
-l-abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve la-bel-wise ROC curves for mul-ti-l-
abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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\T1/pcr/m/sl/10 get_pairwise_correlations[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/
10 Feature.get_pairwise_joint_probabilities[][] \T1/ptm/m/n/10 and

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[]

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[]\T1/ptm/m/n/10 Support for down-load-ing fea-ture dis-cov-ery train-ing or pr
e-dic-tion dataset us-ing [][]\T1/pcr/m/sl/10 Project.
[623]
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[]\T1/ptm/m/n/10 Added [][]\T1/pcr/m/sl/10 datarobot.models.FeatureAssociationM
atrix[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.FeatureAssociationMatrixDetails[][] \T1/ptm/m/n/10 and [
][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Remove \T1/pcr/m/n/10 desired_memory \T1/ptm/m/n/10 param from
 the fol-low-ing meth-ods: [][]\T1/pcr/m/sl/10 CustomInferenceModel.

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\T1/pcr/m/sl/10 create[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersi
on.create_clean[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersion.crea
te_clean[][]\T1/ptm/m/n/10 ,
[624]
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[]\T1/ptm/m/n/10 All per-class lift chart data is now avail-able for mul-ti-cla
ss mod-els us-ing [][]\T1/pcr/m/sl/10 Model.

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\T1/ptm/m/n/10 class can now use \T1/pcr/m/n/10 datarobot.TARGET_TYPE.ANOMALY \
T1/ptm/m/n/10 for its \T1/pcr/m/n/10 target_type \T1/ptm/m/n/10 pa-ram-e-ter.

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[]

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[]\T1/ptm/m/n/10 Add sup-port for start-ing and re-triev-ing Fea-ture Im-pact i
n-for-ma-tion for [][]\T1/pcr/m/sl/10 datarobot.

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[]

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[]\T1/ptm/m/n/10 Actions ap-plied to in-put data dur-ing au-to-mated fea-ture d
is-cov-ery can now be re-trieved us-ing

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[]

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[625]
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[]\T1/ptm/m/n/10 New \T1/pcr/m/n/10 sampling_method \T1/ptm/m/n/10 param in [][
]\T1/pcr/m/sl/10 Model.train_datetime[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 P
roject.train_datetime[][]\T1/ptm/m/n/10 ,

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[]

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\T1/pcr/m/n/10 featurelist_id\T1/ptm/m/n/10 , \T1/pcr/m/n/10 credentials_ids\T1
/ptm/m/n/10 , \T1/pcr/m/n/10 project_version \T1/ptm/m/n/10 and \T1/pcr/m/n/10 
is_default \T1/ptm/m/n/10 in

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[][][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.

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[]\T1/ptm/m/n/10 Class \T1/pcr/m/n/10 FeatureEngineeringGraph \T1/ptm/m/n/10 ha
s been re-moved. Use [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 for

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[]\T1/ptm/m/n/10 for [][]\T1/pcr/m/sl/10 datarobot.models.

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[][][]\T1/pcr/m/sl/10 datarobot.
[626]
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[]\T1/ptm/m/n/10 Added new ar-gu-ment \T1/pcr/m/n/10 base_environment_id \T1/pt
m/m/n/10 to meth-ods [][]\T1/pcr/m/sl/10 CustomModelVersion.

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[]\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 base_environment_id \T1/ptm/m/n/10 a
nd \T1/pcr/m/n/10 dependencies \T1/ptm/m/n/10 to class [][]\T1/pcr/m/sl/10 data
robot.

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[]\T1/ptm/m/n/10 Added a new method to trig-ger Fea-ture Im-pact cal-cu-la-tion
 for a Cus-tom In-fer-ence Im-age:

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[]\T1/ptm/m/n/10 Added new pa-ram-e-ter \T1/ptm/m/it/10 met-ric \T1/ptm/m/n/10 
to [][]\T1/pcr/m/sl/10 datarobot.models.TargetDrift[][]\T1/ptm/m/n/10 , [][]\T1
/pcr/m/sl/10 datarobot.models.
[627]
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[]\T1/ptm/m/n/10 The de-fault value for the \T1/pcr/m/n/10 mode \T1/ptm/m/n/10 
param in [][]\T1/pcr/m/sl/10 Project.set_target[][] \T1/ptm/m/n/10 has been cha
nged from
[628]
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[]\T1/ptm/m/n/10 Added new ar-gu-ments \T1/pcr/m/n/10 explanation_algorithm \T1
/ptm/m/n/10 and \T1/pcr/m/n/10 max_explanations \T1/ptm/m/n/10 to method

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[][]\T1/pcr/m/sl/10 Model.request_training_predictions[][]\T1/ptm/m/n/10 . New 
fields \T1/pcr/m/n/10 explanation_algorithm\T1/ptm/m/n/10 ,

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\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 prediction_explanations \T1/ptm/m/n/10
 and \T1/pcr/m/n/10 shap_metadata \T1/ptm/m/n/10 have been added to class

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[][]\T1/pcr/m/sl/10 TrainingPredictionsIterator[][] \T1/ptm/m/n/10 that is re-t
urned by method [][]\T1/pcr/m/sl/10 TrainingPredictions.

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\T1/pcr/m/sl/10 request_predictions[][]\T1/ptm/m/n/10 . New fields \T1/pcr/m/n/
10 explanation_algorithm\T1/ptm/m/n/10 , \T1/pcr/m/n/10 max_explanations

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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 shap_warnings \T1/ptm/m/n/10 have been added 
to class [][]\T1/pcr/m/sl/10 Predictions[][]\T1/ptm/m/n/10 . Method [][]\T1/pcr
/m/sl/10 Predictions.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 model_splits \T1/ptm/m/n/10 to [][]\T1/pc
r/m/sl/10 DatetimePartitioningSpecification[][] \T1/ptm/m/n/10 and to

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[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Model.request_predictions[][] \T1/ptm/m/n/10 now ac-cept
[629]
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[]\T1/ptm/m/n/10 New warn-ing is added to \T1/pcr/m/n/10 data_quality_warnings 
\T1/ptm/m/n/10 of [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Scores and in-sights on ex-ter-nal test sets can be re-trieved
 us-ing [][]\T1/pcr/m/sl/10 ExternalScores[][]\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 Deployments now sup-port pre-dic-tions data col-lec-tion that 
en-ables pre-dic-tion re-

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\T1/ptm/m/n/10 quests and re-sults to be saved in Pre-dic-tions Data Stor-age. 
See [][]\T1/pcr/m/sl/10 Deployment.

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\T1/pcr/m/sl/10 get_predictions_data_collection_settings[][] \T1/ptm/m/n/10 and
 [][]\T1/pcr/m/sl/10 Deployment.

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[]\T1/ptm/m/n/10 Batch Pre-dic-tion jobs now sup-port scor-ing to Azure and Goo
gle Cloud Stor-age with meth-ods

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[]

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[630]
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[]\T1/ptm/m/n/10 Secondary dataset con-fig-u-ra-tion re-trieve and dele-tion is
 eas-ier now though new

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[][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.delete[][] \T1/ptm/m/n/10 so
ft deletes a Sec-ondary dataset con-fig-u-ra-tion.

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[]

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[631] [632]
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[]\T1/ptm/m/n/10 A new work-flow is avail-able for mak-ing pre-dic-tions in tim
e se-ries projects. To that end,

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[]\T1/ptm/m/n/10 Per-class lift chart data is now avail-able for mul-ti-class m
od-els us-ing [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 A new boolean at-tribute \T1/pcr/m/n/10 unsupervised_mode \T1/
ptm/m/n/10 was added to [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 DatetimePartitioningSpecification[][]\T1/ptm/m/n/10 . When it i
s set to True, date-time par-

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\T1/ptm/m/n/10 ti-tion-ing for un-su-per-vised time se-ries projects will be co
n-structed for now-cast-ing:
[633]
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[]\T1/ptm/m/n/10 Time se-ries model de-ploy-ments now sup-port pre-dic-tion in-
ter-vals. See

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[][]\T1/pcr/m/sl/10 Deployment.get_prediction_intervals_settings[][] \T1/ptm/m/
n/10 and [][]\T1/pcr/m/sl/10 Deployment.

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\T1/ptm/m/n/10 a new op-tional pa-ram-e-ter \T1/pcr/m/n/10 prediction_intervals
_size \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 It’s now pos-si-ble to get fea-ture as-so-ci-a-tion sta-tuse
s for fea-turelists us-ing [][]\T1/pcr/m/sl/10 Project.
[634]
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[]\T1/ptm/m/n/10 Monotonic con-straints are now sup-ported for OTV projects. To
 that end, the pa-ram-e-ters
[635]
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[]\T1/ptm/m/n/10 The method [][]\T1/pcr/m/sl/10 Model.get_supported_capabilitie
s[][] \T1/ptm/m/n/10 now has an ex-tra field
[636] [637] [638]
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[]\T1/ptm/m/n/10 The dataframe re-turned from [][]\T1/pcr/m/sl/10 datarobot.Pre
dictionExplanations.

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[]\T1/ptm/m/n/10 Information re-trieval for [][]\T1/pcr/m/sl/10 ROC Curve[][] \
T1/ptm/m/n/10 has been ex-tended to in-clude

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\T1/pcr/m/n/10 fraction_predicted_as_positive\T1/ptm/m/n/10 , \T1/pcr/m/n/10 fr
action_predicted_as_negative\T1/ptm/m/n/10 ,

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[][][]\T1/pcr/m/sl/10 datarobot.DatetimePartitioningSpecification[][] \T1/ptm/m
/n/10 at-tribute \T1/ptm/m/it/10 de-fault_to_a_priori\T1/ptm/m/n/10 , dep-re-
[639]
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[]\T1/ptm/m/n/10 New class for work-ing with model com-pli-ance doc-u-men-ta-ti
on fea-ture of DataRobot: class

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[]\T1/ptm/m/n/10 Cross se-ries fea-tures can now be cre-ated within time se-rie
s mul-ti-series projects us-ing the

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\T1/pcr/m/n/10 use_cross_series_features \T1/ptm/m/n/10 and \T1/pcr/m/n/10 aggr
egation_type \T1/ptm/m/n/10 at-tributes of the [][]\T1/pcr/m/sl/10 datarobot.
[640]
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[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Project.create[][] \T1/ptm/m/n/10 now ac-cept an op-tional pa-ram-e-te
r of

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[]\T1/ptm/m/n/10 New op-tional pa-ram-e-ter \T1/ptm/m/it/10 fall-back_to_parent
_insights \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Training pre-dic-tions for date-time par-ti-tioned projects no
w sup-port the new data sub-set
[641]
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[]\T1/ptm/m/n/10 New meth-ods and ob-jects, in-clud-ing [][]\T1/pcr/m/sl/10 Mod
el.start_advanced_tuning_session[][]\T1/ptm/m/n/10 ,

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[][]\T1/pcr/m/sl/10 Model.get_advanced_tuning_parameters[][]\T1/ptm/m/n/10 , []
[]\T1/pcr/m/sl/10 Model.advanced_tune[][]\T1/ptm/m/n/10 , and

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[]\T1/ptm/m/n/10 A cus-tom pre-dic-tion thresh-old may now be con-fig-ured for 
each model via [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Individual cross val-i-da-tion scores can be re-trieved for ne
w mod-els us-ing [][]\T1/pcr/m/sl/10 Model.
[642]
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[]\T1/ptm/m/n/10 A new rec-om-men-da-tion type “Rec-om-mended for De-ploy-men
-t” has been added to
[643] [644]
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[]\T1/ptm/m/n/10 Three new op-tions have been made avail-able in the [][]\T1/pc
r/m/sl/10 datarobot.
[645] [646] [647] [648] [649] [650] [651]
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[]\T1/ptm/m/n/10 its
[652] [653]
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[]\T1/pcr/m/n/10 Project.update \T1/ptm/m/n/10 dep-re-cated in fa-vor of spe-ci
fic up-dates: \T1/pcr/m/n/10 rename\T1/ptm/m/n/10 , \T1/pcr/m/n/10 unlock_holdo
ut\T1/ptm/m/n/10 ,
[654] [655]
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[]\T1/pcr/m/n/10 recommendation_settings \T1/ptm/m/n/10 pa-ram-e-ter of \T1/pcr
/m/n/10 Project.start \T1/ptm/m/n/10 is dep-re-cated in fa-vor of
[656] [657] [658] [659] [660]
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[]\T1/pcr/m/n/10 datarobot.models.training_predictions\T1/ptm/m/n/10 ,
[661] (./datarobot-public-api-client.ind [662]
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[]\T1/pcr/m/n/10 AccuracyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotPreview \T1/ptm/m/n/10 (\T1/ptm/m/it/10 cl
ass in

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.Model

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.PrimeModel

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.RatingTableModel

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[]\T1/pcr/m/n/10 AdvancedTuningSession \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in


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[]\T1/pcr/m/n/10 analyze_and_model() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.
models.Project

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[]\T1/pcr/m/n/10 AnomalyAssessmentExplanations \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 AnomalyAssessmentPredictionsPreview

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in datarobot.models.anomaly_assessment\T1
/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyAssessmentRecord \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class 
in

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\T1/ptm/m/it/10 datarobot.models.anomaly_assessment\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlotPreview \T1/ptm/m/n/10 (\T1/ptm/m/it/10 cla
ss in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 c
lass in

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[]\T1/pcr/m/n/10 apply_bias_mitigation()

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[]\T1/pcr/m/n/10 apply_time_series_data_prep_and_score()

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.models.BatchPredictionJob class

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[]\T1/pcr/m/n/10 apply_time_series_data_prep_and_score_to_file()

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.models.BatchPredictionJob class

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[]\T1/pcr/m/n/10 as_dataframe() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.model
s.PairwiseConditionalProbabilities

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[]\T1/pcr/m/n/10 as_dataframe() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.model
s.PairwiseCorrelations

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[]\T1/pcr/m/n/10 as_dataframe() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.model
s.PairwiseJointProbabilities

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[]\T1/pcr/m/n/10 assign_training_data()

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.CustomInferenceModel method\T1/ptm/m/
n/10 ),

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[]\T1/pcr/m/n/10 AutomatedDocument \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.automated_documentation\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 Backtest \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in datarobot.he
lpers.partitioning_methods\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 batch_features_type_transform()

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[]\T1/pcr/m/n/10 BatchPredictionJobDefinition \T1/ptm/m/n/10 (\T1/ptm/m/it/10 c
lass in
[663]
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[]\T1/pcr/m/n/10 BlueprintTaskDocument \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in


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[]\T1/pcr/m/n/10 bucket_sample_sizes

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[1] [2] [1] [2]
Chapter 1.
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Chapter 2.
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[][][]\T1/pcr/m/sl/10 Dataset.create_from_url[][] \T1/ptm/m/n/10 takes .csv dat
a from a URL. If you have set

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[][][]\T1/pcr/m/sl/10 Dataset.create_from_data_source[][] \T1/ptm/m/n/10 takes 
data from a data source. If you have set
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[]\T1/ptm/m/n/10 For mul-ti-class mod-els use \T1/pcr/m/n/10 request_feature_ef
fect_multiclass \T1/ptm/m/n/10 and
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[]\T1/ptm/m/n/10 For mul-ti-class mod-els, you can get a list of per-class lift
 charts us-ing the \T1/pcr/m/n/10 Model \T1/ptm/m/n/10 method
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[]\T1/ptm/m/n/10 After con-fig-ur-ing back-tests, you can set \T1/pcr/m/n/10 us
e_project_settings \T1/ptm/m/n/10 to \T1/pcr/m/n/10 True \T1/ptm/m/n/10 in call
s to [][]\T1/pcr/m/sl/10 Model.
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[]\T1/ptm/m/n/10 For date-time par-ti-tioned model you can re-trieve the Ac-cu-
racy over Time plot. To do so use
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[][][]\T1/pcr/m/sl/10 datarobot.FeatureSettings[][] \T1/ptm/m/n/10 con-struc-to
r re-ceives \T1/ptm/m/it/10 fea-ture_name \T1/ptm/m/n/10 and set-tings. For now
 set-tings
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[]\T1/ptm/m/n/10 Enough rows of his-tor-i-cal data must be pro-vided to cover t
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\T1/ptm/m/n/10 Deriva-tion Win-dow (which may be longer than the pro-jec-t’s 
Fea-ture Deriva-tion Win-dow de-

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\T1/ptm/m/n/10 model can be checked via the \T1/pcr/m/n/10 effective_feature_de
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[]\T1/ptm/m/n/10 Prediction in-ter-vals can be cal-cu-lated for a [][]\T1/pcr/m
/sl/10 DatetimeModel[][] \T1/ptm/m/n/10 us-ing the [][]\T1/pcr/m/sl/10 Datetime
Model.

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[]\T1/ptm/m/n/10 Prediction in-ter-vals are also sup-ported for time se-ries mo
del ex-port. See the op-tional
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[55] [56] [57] [58 <./sample1.png> <./sample2.png>] [59] [60 <./activation_map1
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[]\T1/ptm/m/n/10 Upload a pre-dic-tion dataset that con-tains la-bel col-umn(s)
, re-quest com-pute ex-ter-nal test on one of
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro-cesses=None\T1/pt
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[]\T1/ptm/m/it/10 cluster_name_mappings: List[Tuple[str, str]]\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy
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[]\T1/ptm/m/n/10 Upload a pre-dic-tion dataset that con-tains the tar-get col-u
mn \T1/pcr/m/n/10 PredictionDataset.
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[]\T1/ptm/m/n/10 If you just want to get gen-er-ated pre-dic-tions from a Pre-d
ic-tJob, you can use the \T1/pcr/m/n/10 PredictJob.
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[]\T1/ptm/m/n/10 The method for cre-at-ing job def-i-ni-tions is iden-ti-cal to
 the ex-ist-ing [][]\T1/pcr/m/sl/10 datarobot.models.
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[]\T1/ptm/m/n/10 Alternatively, you can use a short-hand ver-sion with [][]\T1/
pcr/m/sl/10 datarobot.models.
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\T1/ptm/m/n/10 Febru-ary

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[]\T1/ptm/m/n/10 Before ini-ti-at-ing the model re-place-ment re-quest, it is u
su-ally a good idea to use the
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[]\T1/ptm/m/n/10 Refer to [][]\T1/pcr/m/sl/10 ExecutionEnvironment[][] \T1/ptm/
m/n/10 for prop-er-ties of the ex-e-cu-tion en-vi-ron-ment ob-ject and
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[]\T1/ptm/m/it/10 weights: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 re-

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\T1/ptm/m/it/10 sponse_cap: Union[bool\T1/ptm/m/n/10 , \T1/ptm/m/it/10 float\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 None] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_threshold: Op-ti
onal[int]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seed: Op-tional[int] = N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ma-jor-ity_downsampling_rate: Op-tional[float]

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\T1/ptm/m/it/10 ex-po-sure: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 ac-cu-

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\T1/ptm/m/it/10 racy_optimized_mb: Op-tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sca-le-out_modeling_mode: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 events_count: Op-tional[
str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing_feat
urelist_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 only_include_monotonic_b
lueprints:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 tional[List[Tuple[str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ...]]] = 
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blend_best_models: Op-tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-sider_blenders_in_reco
mmendation:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 min_secondary_validation_model_count:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 shap_only
_mode:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 au-topi-


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\T1/ptm/m/it/10 lot_data_sampling_method: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 run_leakage_removed_feat
ure_list:

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\T1/ptm/m/it/10 ture_discovery_supervised_feature_reduction:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-po-ne
n-

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-ter-nal_time_series_bas
eline_dataset_id:

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fair-ness_metrics_set: O
p-tional[str]

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tional[str]

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name: Op-

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[]\T1/ptm/m/n/10 (New in ver-sion v2.23) one of

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\T1/ptm/m/n/10 cu-lat-ing fair-ness. Can be one of \T1/pcr/m/n/10 proportionalP
arity\T1/ptm/m/n/10 , \T1/pcr/m/n/10 equalParity\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 predictionBalance\T1/ptm/m/n/10 , \T1/pcr/m/n/10 trueFavorableAn
dUnfavorableRateParity \T1/ptm/m/n/10 or
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[]\T1/ptm/m/it/10 status:

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\T1/ptm/m/it/10 datarobot.enums.AnomalyAssessmentStatus,

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\T1/ptm/m/it/10 sta-

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[float],

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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[]\T1/ptm/m/n/10 The sta-tus of the in-sight. One of \T1/pcr/m/n/10 datarobot.e
nums.

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[]\T1/ptm/m/it/10 project_id: str, model_id: str, back-test: Union[int,

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\T1/ptm/m/it/10 'hold-out'>][holdout], None] = None, source:

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\T1/ptm/m/it/10 Union[typing_extensions.Literal[<SOURCE_TYPE.TRAINING: 'train-

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\T1/ptm/m/it/10 ing'>][training], typ-ing_extensions.Literal[<SOURCE_TYPE.VALID
ATION:
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[]\T1/ptm/m/n/10 Filter by \T1/ptm/m/it/10 sta-tus \T1/ptm/m/n/10 == Anoma-lyAs
sess-mentSta-

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\T1/ptm/m/it/10 out'>][holdout], None], source: Union[typing_extensions.Literal
[<SOURCE_TYPE.TRAINING:

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\T1/ptm/m/it/10 'train-ing'>][training], typ-ing_extensions.Literal[<SOURCE_TYP
E.VALIDATION:

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.anomaly
_assessment.AnomalyAssessmentPredictionsPreview 

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.anomaly
_assessment.AnomalyAssessmentExplanations 

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[]\T1/ptm/m/it/10 start_date: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 end_date: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 points_count: Op-tional[
int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[158]
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[]\T1/ptm/m/it/10 regions: List[datarobot.models.anomaly_assessment.PreviewBin]
,

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\T1/ptm/m/it/10 pre-dic-tion_threshold: float = 0.0\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.anomaly_assessment.RegionExplanationsData 

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[]

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[]\T1/ptm/m/it/10 shap_base_value:

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\T1/ptm/m/it/10 float,

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\T1/ptm/m/it/10 data:

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\T1/ptm/m/it/10 List[datarobot.models.anomaly_assessment.DataPoint],

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\T1/ptm/m/it/10 start_date:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 tional[str],

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\T1/ptm/m/it/10 **record_kwargs\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/it/10 start_date:

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\T1/ptm/m/it/10 List[datarobot.models.anomaly_assessment.PreviewBin],

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\T1/ptm/m/it/10 **record_kwargs\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/it/10 max_prediction_threshold: float = 0.0\T1/pcr/m/n/10 ) $\OMS/c
msy/m/n/10 !$

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[]\T1/ptm/m/n/10 greater or

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 out-put_settings: Op-tio
nal[OutputSettings] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 thresh-old_low: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 pre-dic-tion_warning_enabled:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-clude
_prediction_status: bool = False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 skip_drift_tracking: bool = False\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 pre-dic-tion_instance: Op-

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\T1/ptm/m/it/10 tional[PredictionInstance] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 abort_on_error: bool = True\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 col-umn_names_remapping: Op-tional[Dict[str\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 str]] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-
[162] [163] [164] [165]
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\T1/ptm/m/it/10 time-series_settings: Time-
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[]\T1/ptm/m/it/10 deployment:

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\T1/ptm/m/it/10 De-ploy-ment,

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\T1/ptm/m/it/10 Union[str,

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\T1/ptm/m/it/10 pd.DataFrame,

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\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Batch-Pre-dic-
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[]\T1/ptm/m/n/10 [string] The URL for the pre-dic-tion dataset (e.g.:

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[]\T1/ptm/m/n/10 [string] The URL for the scored dataset (e.g.:

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[]\T1/ptm/m/n/10 [string] The URL for the pre-dic-tion dataset (e.g.:

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[]\T1/ptm/m/n/10 [string] The URL for the scored dataset (e.g.:

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[]\T1/ptm/m/it/10 batch_prediction_job_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/1
0 !$

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\T1/ptm/m/n/10 datarobot.models.batch_prediction_job.BatchPredictionJob 
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[]\T1/ptm/m/it/10 batch_prediction_job_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/1
0 !$ \T1/ptm/m/n/10 datarobot.models.batch_prediction_job.BatchPredictionJob 

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[]\T1/ptm/m/it/10 statuses: Op-tional[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/c
msy/m/n/10 !$

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\T1/ptm/m/it/10 batch_prediction_job=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-ated: Op-tional[str] =

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated_by=None\T1/ptm/
m/n/10 ,

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\T1/ptm/m/it/10 up-dated_by=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 last_failed_run_time:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 last_successful_run_time:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 last_started_job_status:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 last_scheduled_run_time:
[171]
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[]\T1/ptm/m/it/10 batch_prediction_job_definition_id: str\T1/pcr/m/n/10 ) $\OMS
/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 nodes: List[Dict[str, str]], edges:

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.blueprint
.ModelBlueprintChart 
[179]
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[]\T1/ptm/m/it/10 file_path: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-en-dar_nam
e: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mul-ti-series_id_columns: Op-tional[List[str]] = None\T1/pcr/m/
n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/n/10 Once the Cal-en-darFile has been cre-ated, pass its ID with th
e
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[]\T1/ptm/m/it/10 dataset_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_versi
on_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-en-

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\T1/ptm/m/it/10 dar_name: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mul-ti-series_id_columns: Op-

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\T1/ptm/m/it/10 Op-tional[bool] = False\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.calendar_file.CalendarFile 

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[]\T1/ptm/m/n/10 Once the Cal-en-darFile has been cre-ated, pass its ID with th
e
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[]\T1/ptm/m/it/10 country_code: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_date:


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\T1/ptm/m/it/10 date-time.datetime\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_date:

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\T1/ptm/m/it/10 date-time.datetime\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.calendar_file.CalendarFile 
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[]\T1/ptm/m/it/10 entity_id:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 u-ment_type:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 put_format:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tem-plate_id:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 filepath:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 tional[datetime.datetime]
[185]
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[]\T1/ptm/m/n/10 [str or None] Type of au-to-mated doc-u-ment. You can spec-ify
:

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 List[datarobot.models.au
tomated_documentation.DocumentOption] 
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[]\T1/ptm/m/n/10 Required at-tributes to re-quest doc-u-ment gen-er-a-tion: \T1
/pcr/m/n/10 document_type\T1/ptm/m/n/10 , \T1/pcr/m/n/10 entity_id\T1/ptm/m/n/1
0 , and
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[]

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[]\T1/ptm/m/it/10 max_unaggregated_class_values:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 min_class_support:

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\T1/ptm/m/it/10 cluded_from_aggregation:

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\T1/ptm/m/it/10 Op-tional[List[str]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ag-gre-ga-

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\T1/ptm/m/it/10 tion_class_name: Op-
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[]\T1/ptm/m/n/10 Authentication to-ken type: To-ken, Bearer.
[191]
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[]\T1/ptm/m/it/10 client: datarobot.rest.RESTClientObject\T1/pcr/m/n/10 ) $\OMS
/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Op-

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[]\T1/ptm/m/it/10 auth: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end-point: str\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 con-nect_timeout:

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\T1/ptm/m/it/10 Op-tional[int] = 6.05\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ver-ify: 
bool = True\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 user_agent_suffix: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_retries: Union[int\T1/ptm/m/n/10 , \T1/ptm/m/it/10 url-lib3
.util.retry.Retry\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-

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\T1/ptm/m/it/10 ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_sta
rt_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_t
ype=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_n_clusters_dynamically_determined=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 n_clusters=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 has_empty_clust
ers=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_project_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 cluster_name_mappings: List[Tuple[str, str]]\T1/pcr/m/n/10 ) 
$\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 current_name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 new_name: s
tr\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 project_id: str, model_id: str, clus-

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\T1/ptm/m/it/10 ter_name_mappings: List[Tuple[str, str]]\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy
/m/n/10 !$
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\T1/ptm/m/it/10 ator_username:

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\T1/ptm/m/it/10 name:

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\T1/ptm/m/it/10 org_id:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[str]

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\T1/ptm/m/it/10 =

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\T1/ptm/m/it/10 sec-

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\T1/ptm/m/it/10 tions:

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\T1/ptm/m/it/10 Op-

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\T1/ptm/m/it/10 tional[List[datarobot.models.compliance_doc_template.Section]]

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\T1/ptm/m/it/10 =
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[]\T1/ptm/m/it/10 template_type: Op-tional[datarobot.enums.ComplianceDocTemplat
eType]
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[]\T1/ptm/m/it/10 name: str, sec-tions: List[datarobot.models.compliance_doc_te
mplate.Section]\T1/pcr/m/n/10 )

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[]\T1/ptm/m/it/10 name: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
ec-tions: Op-tional[List[datarobot.models.compliance_doc_template.Section]]
[198]
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[]\T1/ptm/m/it/10 source:

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\T1/ptm/m/it/10 datarobot.enums.CHART_DATA_SOURCE\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data:

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\T1/ptm/m/it/10 datarobot.models.confusion_chart.ConfusionChartRawData\T1/ptm/m
/n/10 ,

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[]

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[]\T1/pcr/m/n/10 [ [ True Negative, False Positive ], [ False Negative, True
[199] [200] [201]
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[]\T1/ptm/m/it/10 name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 aws_access_key_id: 
Op-tional[str] = None\T1/ptm/m/n/10 ,
[202]
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\T1/ptm/m/it/10 file_path\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 file_source\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-

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\T1/ptm/m/it/10 ated_at=None\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/n/10 target type of the cus-tom in-fer-ence model.

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\T1/ptm/m/it/10 datarobot.TARGET_TYPE.MULTICLASS\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 datarobot.TARGET_TYPE.UNSTRUCTURED\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 Target fea-ture name; it is op-tional(ignored if pro-vided)

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[]\T1/ptm/m/n/10 Determines
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 tar-get_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guag
e=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-

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\T1/ptm/m/it/10 tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i-tive_class_labe
l=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 neg-a-tive_class_label=None\T1/ptm/m/n/1
0 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cla
ss_labels=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_labels_file=None\T1/ptm/m/
n/10 ,
[205]
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[]\T1/ptm/m/n/10 target type of the cus-

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\T1/ptm/m/n/10 tom in-fer-ence model. Val-ues: [\T1/ptm/m/it/10 datarobot.TARGE
T_TYPE.BINARY\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 datarobot.TARGET_TYPE.REGRESSION\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 datarobot.TARGET_TYPE.MULTICLASS\T1/ptm/m/n/10 ,

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[]

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[]\T1/ptm/m/it/10 name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 lan-guage=None\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
tar-get_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i-

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\T1/ptm/m/it/10 tive_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 neg-a-tiv
e_class_label=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_threshold=None\
T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 the type of the im-age, ei-ther CUS-TOM_MODEL_IMAGE_TYPE.CUSTO
M_MODEL_IMAGE

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\T1/ptm/m/n/10 if the test-ing at-tempt is us-ing a Cus-tom-Mod-e-lIm-age as it
s model or CUS-

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[]\T1/ptm/m/n/10 Determines

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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/n/10 ,
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[]
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[]\T1/ptm/m/n/10 Determines
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\T1/ptm/m/it/10 folder_path=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 files=None\T1/
ptm/m/n/10 , \T1/ptm/m/it/10 net-work_egress_policy=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max-i-mum_memory=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 repli-cas
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/n/10 “-file1.txt”), (“/home/user/Documents/myModel/folder/file2
.txt”,

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\T1/ptm/m/n/10 “-folder/file2.txt”)] or [“/home/user/Documents/myModel/fi
le1.txt”,

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[]

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 base_environm
ent_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 files_to_delete=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 net-work_e
gress_policy=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max-i-mum_memory=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 repli-cas
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/n/10 “-folder/file2.txt”)] or [“/home/user/Documents/myModel/fi
le1.txt”,

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[]

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/it/10 custom_model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cus-tom_model
_version_id\T1/ptm/m/n/10 ,
[215] [216] [217]
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[]\T1/ptm/m/it/10 name\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 pro-gram-ming_language=None\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 re-
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[]\T1/ptm/m/it/10 execution_environment_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dock
er_context_path\T1/ptm/m/n/10 , \T1/ptm/m/it/10 la-bel=None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 de-scrip-
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\T1/ptm/m/n/10 See datarobot.enums.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS
[221]
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[]\T1/ptm/m/it/10 id: str, tar-get_type: datarobot.enums.CUSTOM_TASK_TARGET_TYP
E,

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\T1/ptm/m/it/10 lat-est_version: Op-tional[datarobot.models.custom_task_version
.CustomTaskVersion],

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\T1/ptm/m/it/10 datarobot.enums.Enum, cre-ated_by: str, cal-i-brate_predictions
:

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\T1/ptm/m/n/10 tween 0 and 1 by DR. only ap-plies to cus-tom es-ti-ma-tors with
 tar-get type
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[]\T1/ptm/m/it/10 data: Union[Dict[str, Any], List[Dict[str, Any]]],

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\T1/ptm/m/it/10 keep_attrs: Op-tional[Iterable[str]] = None\T1/pcr/m/n/10 ) $\O
MS/cmsy/m/n/10 !$
[223]
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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-i-brate_
predictions: Op-tional[bool] = None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 the tar-get typed

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[]\T1/ptm/m/n/10 whether anomaly pre-dic-tions should be

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\T1/ptm/m/n/10 cal-i-brated to be be-tween 0 and 1 by DR. if None, uses de-faul
t value

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\T1/ptm/m/n/10 from DR app (True). only ap-plies to cus-tom es-ti-ma-tors with 
tar-get type
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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 file_name\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 file_path\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 file_source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-

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\T1/ptm/m/it/10 bel\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated_at\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 is_frozen\T1/ptm/m/n/10 , \T1/ptm/m/it/10 items\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 de-scrip-tion=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 base_environment_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max-i-
mum_memory=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 is_major_update=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 folder_pat
h=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-

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[]\T1/ptm/m/it/10 custom_task_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 base_environme
nt_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_major_update=True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 folder_pat
h=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 files_to_delete=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/it/10 quired_metadata_values=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max
-i-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/n/10 Additional pa-ram-e-ters re-
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[]\T1/ptm/m/it/10 class_name: str, canon-i-cal_name: str, files: List[str]\T1/p
cr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[231] [232] [233] [234]
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[]\T1/ptm/m/n/10 [str] the full JDBC url, for ex-am-ple \T1/ptm/m/it/10 jdbc:po
stgresql://my.dbaddress.org:5432/my_db\T1/ptm/m/n/10 . 

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[]\T1/ptm/m/n/10 [str] op-tional, the full JDBC url, for ex-am-ple
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[]\T1/ptm/m/it/10 data: Union[Dict[str, Any], List[Dict[str, Any]]],

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\T1/ptm/m/it/10 keep_attrs: Op-tional[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/c
msy/m/n/10 !$

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[]\T1/ptm/m/it/10 data_source_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 data_source_type:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 canon-i-c
al_name: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ator: Op-tional[str]
 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 up-dated:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 params: Op-

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\T1/ptm/m/it/10 tional[datarobot.models.data_source.DataSourceParameters] =
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[]\T1/ptm/m/it/10 data_source_type: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 canon-i
-cal_name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 params:
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[]\T1/ptm/m/it/10 canonical_name: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 params: Op-
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories: Op-tional[
List[str]] = None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 data_frame: Op-

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\T1/ptm/m/it/10 tional[pandas.core.frame.DataFrame] =
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[]\T1/ptm/m/it/10 url: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 do_snapshot: Op-tion
al[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 per-

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pass-word: Op-tional[str
] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 do_snapshot: Op-tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 per-
[244] [245] [246] [247]
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[]\T1/ptm/m/it/10 order_by: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/
n/10 !$ \T1/ptm/m/n/10 List[datarobot.models.feature.DatasetFeature] 

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[]\T1/ptm/m/it/10 offset: Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 limit: Op-tional[int] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 or-der_by: Op-tional[str] 
= None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Gen-er-a-
[248]
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[]\T1/ptm/m/it/10 name: str, fea-tures: List[str]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/
n/10 !$ \T1/ptm/m/n/10 datarobot.models.featurelist.DatasetFeaturelist 
[249] [250]
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\T1/ptm/m/it/10 tional[pandas.core.frame.DataFrame]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 records: Op-

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\T1/ptm/m/it/10 tional[List[Dict[str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 Any]]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cat-e-gories: Op-

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\T1/ptm/m/it/10 tional[List[str]] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 read_timeout: int = 600\T1/ptm/m/n/10 ,
[251]
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\T1/ptm/m/it/10 user-name: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cre-den-tial_data: Op-tional[Dict[str\T1/ptm/m/n/10 ,
[252]
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\T1/ptm/m/it/10 ated_by: str, cre-ated_at: str, data_source_type: str,

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\T1/ptm/m/it/10 is_data_engine_eligible: bool, last_modification_date:

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\T1/ptm/m/it/10 pro-cess-ing_state: str, data_persisted: Op-tional[bool]

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\T1/ptm/m/it/10 = None, data_engine_query_id: Op-tional[str] = None,

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\T1/ptm/m/it/10 Op-tional[List[datarobot.models.dataset.FeatureTypeCount]] =
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[]\T1/ptm/m/n/10 the uri to data-source like: - ‘-file_name.csv’ - ‘[][]$
jdbc : DATA _
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: s
tr\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dataset_version_id: Op-tional[str] =

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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check:

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\T1/ptm/m/it/10 Op-tional[bool] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.prediction_dataset.PredictionDataset 

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[]\T1/ptm/m/it/10 sourcedata: Union[str, pan-das.core.frame.DataFrame,

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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check: Op-tional[bool] =
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fore-

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\T1/ptm/m/it/10 cast_distance\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 res-

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\T1/ptm/m/it/10 o-

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\T1/ptm/m/it/10 lu-

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\T1/ptm/m/it/10 tions\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-

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\T1/ptm/m/it/10 test_metadata\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_metadata\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 back-

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\T1/ptm/m/it/10 test_statuses\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_statuses\T1/pcr/m/n/10 ) 
[257]
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[]\T1/ptm/m/n/10 Status back-test/holdout train-ing. One of \T1/pcr/m/n/10 data
robot.enums.

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[]\T1/ptm/m/n/10 Status back-test/holdout val-i-da-tion. One of \T1/pcr/m/n/10 
datarobot.enums.

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[]\T1/ptm/m/n/10 A list of \T1/pcr/m/n/10 datarobot.enums.
[258]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 ,

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[260]
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[]
[270] [271]
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-ment.

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\T1/ptm/m/n/10 To cre-ate or up-date ex-ist-ing ‘’-tar-get_drift’’ set-
tings, see
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time.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-e-cu-

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tetime.datetime] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_time: Op-tional[dateti
me.datetime] = None\T1/ptm/m/n/10 ,

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al[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[280]
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lanations: bool = False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit: in
t = 100\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: int = 0\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$

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hresh-old: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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tm/m/n/10 , \T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 deployment_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: 
Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 start_time: Op-tional[datetime.datetime] = None\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 end_time: Op-
[287]
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_classes: Op-tion
al[List[str]] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[288]
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_time: Op-tional[date
time.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 bucket_size:
[289]
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[]\T1/ptm/m/it/10 deployment_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics: 
Op-

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\T1/ptm/m/it/10 tional[List[datarobot.enums.ACCURACY_METRIC]] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: Op-tional[str] =
 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_time:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 end_time: Op-

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\T1/ptm/m/it/10 share_recipient_type: str\T1/ptm/m/n/10 ,

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[]
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[]\T1/ptm/m/it/10 id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 role: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 share_recipient_type:

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[]

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[]

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[]\T1/ptm/m/it/10 role:

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\T1/ptm/m/it/10 user-

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\T1/ptm/m/it/10 name:

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\T1/ptm/m/it/10 **kwargs\T1/pcr/m/n/10 ) 
[291]
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[]

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: Op-
tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: 
int = 0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit: int = 100\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.external_dataset_scores_insights.external_
scores.ExternalScores] 

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n
/10 !$

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\T1/ptm/m/n/10 datarobot.models.external_dataset_scores_insights.external_score
s.ExternalScores 
[292]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: int = 0\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 limit: int = 100\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.external_dataset_scores_insights.external_
lift_chart.ExternalLiftChart] 

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n
/10 !$

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\T1/ptm/m/n/10 datarobot.models.external_dataset_scores_insights.external_lift_
chart.ExternalLiftChart 

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[]\T1/ptm/m/it/10 dataset_id: str, roc_points: List[EstimatedMetric],

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\T1/ptm/m/it/10 neg-a-tive_class_predictions: List[float], pos-i-
[293]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: int = 0\T1/ptm/
m/n/10 , \T1/ptm/m/it/10 limit: int = 100\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.external_dataset_scores_insights.external_
roc_curve.ExternalRocCurve] 

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n
/10 !$

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\T1/ptm/m/n/10 datarobot.models.external_dataset_scores_insights.external_roc_c
urve.ExternalRocCurve 

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=None\T1/ptm/m/n
/10 , \T1/ptm/m/it/10 name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_type=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 im-por-tance=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 low_informati
on=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 unique_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 na_count=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date_format=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 min=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max=None\T1/ptm/m/n/10
 , \T1/ptm/m/it/10 mean=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 me-

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\T1/ptm/m/it/10 dian=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 std_dev=None\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 time_series_eligible=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 time_series_eligibility_reason=None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 time_step=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 time_unit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_leakage=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_lineage_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 key_summar
y=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-ti-l-
[294]
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[]\T1/ptm/m/n/10 {‘key’:’DataRobot’, ‘-sum-ma-ry’:{‘min’:0, ‘
-
[295]
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[]\T1/ptm/m/it/10 datetime_partition_column\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cro
ss_series_group_by_columns\T1/ptm/m/n/10 ,
[296] [297]
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\T1/ptm/m/it/10 im-por-tance=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 low_informati
on=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 unique_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 na_count=None
\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 date_format=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 min=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 max=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mean=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 me-dian=None\T1/ptm/m
/n/10 , \T1/ptm/m/it/10 std_dev=None\T1/ptm/m/n/10 ,
[298]
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[]\T1/ptm/m/n/10 {‘key’:’DataRobot’, ‘-sum-ma-ry’:{‘min’:0, ‘
-

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[]\T1/ptm/m/it/10 id_\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id=None\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 dataset_version_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_type=None\T1
/ptm/m/n/10 ,

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\T1/ptm/m/it/10 low_information=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 unique_cou
nt=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 na_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date_format=None\
T1/ptm/m/n/10 , \T1/ptm/m/it/10 min_=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mean=None\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 me-dian=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 std_dev=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_series_eligib
le=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 time_series_eligibility_reason=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 time_step=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_unit=None\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-
[299] [300] [301] [302]
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[]\T1/ptm/m/n/10 [list(dict)] List of cor-re-la-tion val-ues with a schema de-s
cribed as
[303]
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[]\T1/ptm/m/n/10 [list(dict)] List of joint prob-a-bil-ity val-ues with a schem
a de-scribed as

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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0,B=1) can be re-trieved via:

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\T1/pcr/m/n/10 pairwise_joint_probabilities.statistic_dataframes[(0,1)].

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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0,B=1) can be re-trieved via:

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\T1/pcr/m/n/10 pairwise_joint_probabilities.as_dataframe((0,1)).loc[\TS1/pcr/m/
n/10 '\T1/pcr/m/n/10 A\TS1/pcr/m/n/10 '\T1/pcr/m/n/10 ,
[304]
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[]\T1/ptm/m/n/10 The joint prob-a-bil-i-ties for the re-quested

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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0|B=1) can be re-trieved via:

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\T1/pcr/m/n/10 pairwise_conditional_probabilities.statistic_dataframes[(0,

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[]\T1/ptm/m/n/10 You might find it more con-ve-nient to use [][]\T1/pcr/m/sl/10
 Feature.get_pairwise_conditional_probabilities[][]
[305]
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[]\T1/ptm/m/n/10 E.g. The prob-a-bil-ity P(A=0|B=1) can be re-trieved via: \T1/
pcr/m/n/10 pairwise_conditional_probabilities.

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[]\T1/ptm/m/n/10 The con-di-tional prob-a-bil-i-ties for the re-quested

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[]\T1/ptm/m/it/10 strengths: Op-tional[List[Strength]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures: Op-

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\T1/ptm/m/it/10 tional[List[Feature]] = None\T1/ptm/m/n/10 ,
[306]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-ric: Op-t
ional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 as-so-ci-a-tion_type:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turel
ist_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.feature_association_matrix.feature_association_
matrix.FeatureAssociationMatrix 

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[]\T1/ptm/m/n/10 [enums.FEATURE_ASSOCIATION_METRIC] The name of a met-ric to

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[]\T1/ptm/m/n/10 [enums.FEATURE_ASSOCIATION_TYPE] The type of de-

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[]\T1/ptm/m/it/10 project_id: Op-tional[str]

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\T1/ptm/m/it/10 Op-tional[List[Tuple[Any\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 tures: Op-tional[List[str]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 types: Op-

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\T1/ptm/m/it/10 tional[List[str]] = None\T1/ptm/m/n/10 ,
[307]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture1: st
r\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture2: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-turelist_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cm
sy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.feature_association_matrix.feature_association_
matrix_details.FeatureAssociationMatrixDetails 

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[]\T1/ptm/m/it/10 project_id: Op-tional[str]

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\T1/ptm/m/it/10 tional[List[FeatureListType]]

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[]\T1/ptm/m/it/10 project_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/
m/n/10 datarobot.models.feature_association_matrix.feature_association_featurel
ists.FeatureAssociationFeaturelists 

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_definitions=None\T
1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 re-la-tion-ships=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_discovery_mode=None\T1/ptm/m/n/10 ,
[308] [309]
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[]\T1/ptm/m/n/10 List of fea-ture deriva-tion win-

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\T1/ptm/m/n/10 dows set-tings. If present, time-aware joins will be used. Only 
al-lowed

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\T1/ptm/m/n/10 when fea-ture_derivation_window_start, fea-ture_derivation_windo
w_end and fea-

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[]\T1/ptm/m/n/10 Closest value of pre-dic-
[310]
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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.helpers.

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[]\T1/ptm/m/n/10 [list of fea-ture dis-cov-ery set-tings, op-tional] Each el-

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\T1/ptm/m/n/10 e-ment is a dic-tio-nary or a \T1/pcr/m/n/10 datarobot.helpers.f
eature_discovery.
[311]
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[]\T1/ptm/m/n/10 The re-quested re-la-tion-ships

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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Each el-e-ment is a \T1/pcr/m/n/10 datarobot.helpers.

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[]\T1/ptm/m/n/10 [list of fea-ture dis-cov-ery set-tings, op-tional] Each el-

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\T1/ptm/m/n/10 e-ment is a dic-tio-nary or a \T1/pcr/m/n/10 datarobot.helpers.f
eature_discovery.
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[]\T1/ptm/m/it/10 identifier: str,

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\T1/ptm/m/it/10 cat-a-log_id: Op-

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\T1/ptm/m/it/10 tional[str], cat-a-

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\T1/ptm/m/it/10 snap-shot_policy:

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\T1/ptm/m/it/10 str = 'lat-est',

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\T1/ptm/m/it/10 fea-ture_list_id:

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\T1/ptm/m/it/10 Op-tional[str]

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\T1/ptm/m/it/10 = None, pri-

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\T1/ptm/m/it/10 mary_temporal_key:

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\T1/ptm/m/it/10 Op-tional[str] =
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[]\T1/ptm/m/it/10 dataset2_identifier: str,

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\T1/ptm/m/it/10 dataset1_keys: List[str],

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\T1/ptm/m/it/10 dataset2_keys: List[str],

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\T1/ptm/m/it/10 dataset1_identifier: Op-

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\T1/ptm/m/it/10 tional[str] = None, fea-

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\T1/ptm/m/it/10 ture_derivation_window_start:

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\T1/ptm/m/it/10 ture_derivation_window_end:

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\T1/ptm/m/it/10 ture_derivation_window_time_unit:

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\T1/ptm/m/it/10 ture_derivation_windows:

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\T1/ptm/m/it/10 Op-tional[List[Dict[str,

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\T1/ptm/m/it/10 Union[int, str]]]]

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\T1/ptm/m/it/10 = None, pre-dic-

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\T1/ptm/m/it/10 tion_point_rounding_time_unit:
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[]\T1/ptm/m/n/10 Column(s) from the sec-ond

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[]\T1/ptm/m/n/10 List of fea-ture deriva-tion win-

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\T1/ptm/m/n/10 dows set-tings. If present, time-aware joins will be used. Only 
al-lowed

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\T1/ptm/m/n/10 when fea-ture_derivation_window_start, fea-ture_derivation_windo
w_end and fea-

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[]\T1/ptm/m/n/10 Closest value of pre-dic-
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[]\T1/ptm/m/it/10 id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id:

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\T1/ptm/m/it/10 str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-fig: Op-

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\T1/ptm/m/it/10 tional[List[DatasetConfiguration]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sec-

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\T1/ptm/m/it/10 ondary_datasets: Op-

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\T1/ptm/m/it/10 tional[List[SecondaryDataset]]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-ated: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cre-den-tial_ids:

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\T1/ptm/m/it/10 Op-tional[StoredCredentials]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_default: Op-

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\T1/ptm/m/it/10 tional[bool] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_version: Op-

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[]\T1/ptm/m/n/10 List of sec-ondary datasets

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[]\T1/ptm/m/n/10 credentials used by the sec-ondary
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[]\T1/ptm/m/it/10 project_id: str, sec-ondary_datasets: List[datarobot.helpers.
feature_discovery.SecondaryDataset],

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\T1/ptm/m/it/10 name: str, fea-turelist_id: Op-tional[str] = None\T1/pcr/m/n/10
 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_
id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set: 
Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/n/10 [list of Sec-ondary-Dataset-Con-fig-u-ra-tions] The re-

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\T1/ptm/m/it/10 log_version_id: str\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 ff_metadata_datetime_per_backtest\T1/pcr/m/n/10 ) 

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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_fit\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 back-
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[]\T1/ptm/m/it/10 ff_metadata_datetime_per_backtest\T1/pcr/m/n/10 ) 
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[]

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[]

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[]\T1/ptm/m/it/10 total_restore_limit:

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\T1/ptm/m/it/10 re-

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\T1/ptm/m/it/10 main-

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\T1/ptm/m/it/10 ing_restore_limit:

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\T1/ptm/m/it/10 count:

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\T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures:

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\T1/ptm/m/it/10 List[str]\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/it/10 project_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/
m/n/10 datarobot.models.restore_discarded_features.DiscardedFeaturesInfo 
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[]\T1/ptm/m/it/10 data\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-pleted_resource_url=
None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 missing_values_report:

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\T1/ptm/m/it/10 List[MissingReportPerFeatureDict]\T1/pcr/m/n/10 ) 
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.missing_r
eport.MissingValuesReport 

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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro-cesses=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 fea-turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 fea-

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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_n_clusters_dynamically_determined=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 n_clusters=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 has_empty_clust
ers=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-

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\T1/ptm/m/it/10 ports_monotonic_constraints=None\T1/ptm/m/n/10 , \T1/ptm/m/it/1
0 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tio
n_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 ,
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[]
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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the

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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
[343]
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[]

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[]
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 train-

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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 score=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasin
g_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
odel_number=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 train-

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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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\T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id
=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-

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\T1/ptm/m/it/10 ple_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_coun
t=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-

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\T1/ptm/m/it/10 sam-pling_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_typ
e=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 out_score=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_status=
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data_selection_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back
-tests=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mono-tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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/it/10 ef-fec-

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\T1/ptm/m/it/10 fore-cast_window_start=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 for
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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model
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\T1/ptm/m/it/10 ports_composable_ml=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 n_clus
ters=None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/n/10 [str or None] (New in v2.23) in-di-cates the way train-ing dat
a
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[]\T1/ptm/m/it/10 time_window_sample_pct=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 f
ea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1
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\T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-i
ng_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_end_date=None\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 sam-

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[]\T1/ptm/m/it/10 backtest_index\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None
\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest_index\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source\T1/ptm/
m/n/10 ,

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\T1/ptm/m/it/10 top_n_features=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-tures=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 class_=None\T1/p
tm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:

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\T1/ptm/m/it/10 'val-i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_dist
ance_start=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-
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[]\T1/ptm/m/n/10 [AccuracyOverTimePlotsMetadata] a [][]\T1/pcr/m/sl/10 Accuracy
OverTimePlotsMetadata[][]

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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
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\T1/ptm/m/it/10 i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_distance=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 se-ries_id=None\T1/ptm/m/n/10 , \T1/ptm/m/
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\T1/ptm/m/it/10 o-lu-tion=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_bin_size=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_date=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:

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\T1/ptm/m/it/10 'val-i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_dist
ance=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 se-
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:

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\T1/ptm/m/it/10 'val-i-da-tion'>\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_dist
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\T1/ptm/m/it/10 max_bin_size=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start_date=No
ne\T1/ptm/m/n/10 , \T1/ptm/m/it/10 end_date=None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 backtest=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source=<SOURCE_TYP
E.VALIDATION:
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[]\T1/ptm/m/it/10 prediction_threshold=0.5\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back
-test=0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 source=<SOURCE_TYPE.VALIDATION: 'val-i-
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from

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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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\T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-
ing_duration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-ing_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_category=
None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_frozen=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 blueprint_id=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-

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\T1/ptm/m/it/10 rics=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ent_model_id=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/
it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tion_threshold_read_only=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 m
odel_number=None\T1/ptm/m/n/10 ,
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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_name: Op-tional[st
r] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/
10 note: Op-

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 im-ported_by_username: Op-
tional[str] =

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\T1/ptm/m/it/10 ated_by_username: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 cre-
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[]\T1/ptm/m/it/10 path: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: int = 600
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 limit: Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 
off-set: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
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[]\T1/ptm/m/it/10 id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro-cesses=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 turelist_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist
_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=Non
e\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_du
ration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 train-ing_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train
-

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\T1/ptm/m/it/10 ing_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_type=No
ne\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_category=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 is_frozen=N
one\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 blueprint_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 met-rics=None
\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 rat-ing_table_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/
it/10 mono-

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\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 sup-ports_monotonic_constraints=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 is_starred=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_th
reshold=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_threshold_read_only=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_number=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sup-
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[]

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[]\T1/ptm/m/it/10 source\T1/ptm/m/n/10 , \T1/ptm/m/it/10 top_n_features=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 tures=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count=None\T1/pt
m/m/n/10 ,
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[]
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[]\T1/ptm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 train-

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\T1/ptm/m/it/10 Op-tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 trai
n-ing_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 time_wi
ndow_sample_pct:
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the
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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_expla
nations: Op-tional[int] = None\T1/ptm/m/n/10 ,
[463]
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[]\T1/ptm/m/it/10 data_subset\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pla-na-tion_al
gorithm=None\T1/ptm/m/n/10 ,

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[]
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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-
[466]
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the

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\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor
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[]\T1/ptm/m/n/10 [dict] “-Pa-ram-e-ter-s” dic-tio-nary, same as spec-i-fied
 on

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[]

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_job_id: str\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 eureqa_solution_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-plex-i
ty\T1/ptm/m/n/10 , \T1/ptm/m/it/10 er-

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\T1/ptm/m/it/10 ror\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-pres-sion\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 ex-pres-sion_annotated\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 datetime_partition_column: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 au-topi-lot_data_selection_method:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 val-i-da-


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\T1/ptm/m/it/10 tion_duration: Op-tional[str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_start_date=None\T
1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_duration: Op-tional[str]

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num-

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\T1/ptm/m/it/10 ber_of_backtests: Op-tional[int]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back-tests=None\T1/ptm/m
/n/10 ,

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\T1/ptm/m/it/10 bool = False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_derivation_window_start:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_derivation_window_end:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-

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\T1/ptm/m/it/10 ture_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_window_end:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 win-

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\T1/ptm/m/it/10 dows_basis_unit: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 treat_as_exponential:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dif-fer-

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\T1/ptm/m/it/10 enc-ing_method: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pe-ri-od-ic-i-ties=None\
T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 mul-ti-series_id_columns: Op-

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\T1/ptm/m/it/10 tional[List[str]] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_cross_series_features: Op-

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\T1/ptm/m/it/10 tional[bool] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ag-gre-ga-

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\T1/ptm/m/it/10 cross_series_group_by_columns:

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\T1/ptm/m/it/10 Op-tional[List[str]] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cal-en-dar_id: Op-tional[str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_end_date=None\T1/
ptm/m/n/10 ,

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\T1/ptm/m/it/10 un-su-per-vised_mode: bool

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\T1/ptm/m/it/10 = False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_splits: Op-

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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 low_partial_history_time_series_predictions:
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[]\T1/ptm/m/n/10 This is the spec-i-fi-ca-tion that should be passed to [][]\T1
/pcr/m/sl/10 Project.analyze_and_model[][] \T1/ptm/m/n/10 via the

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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the

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[]\T1/ptm/m/n/10 Note that ei-ther (\T1/pcr/m/n/10 holdout_start_date\T1/ptm/m/
n/10 , \T1/pcr/m/n/10 holdout_duration\T1/ptm/m/n/10 ) or (\T1/pcr/m/n/10 holdo
ut_start_date\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-

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[]\T1/ptm/m/n/10 [datetime.datetime or None] The start date of hold-out scor-in
g

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[]\T1/ptm/m/n/10 [str or None] The du-ra-tion of the hold-out scor-ing data. If

[477]
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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of

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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)
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\T1/ptm/m/it/10 val-i-da-tion_end_date: Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri-mary_training_start_
date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pri-mary_training_end_date: Op-

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\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string s
uch as those re-turned by the

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[]\T1/ptm/m/n/10 Option 2 (New in ver-sion v2.20): Use \T1/pcr/m/n/10 index\T1/
ptm/m/n/10 , \T1/pcr/m/n/10 primary_training_start_date\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 , and \T1/pcr/m/n/10 validation_end_date\T1/ptm/m/
n/10 .
[479]
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[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the

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[]\T1/ptm/m/n/10 [bool][Switch to us-ing the older a_priori key name in-stead o
f
[480]
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[]\T1/ptm/m/it/10 project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date-time_par
titioning_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 au-topi-lot_data_selection_method=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ava
il-

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\T1/ptm/m/it/10 able_training_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 a
vail-

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\T1/ptm/m/it/10 able_training_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ava
il-

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\T1/ptm/m/it/10 able_training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 av
ail-

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\T1/ptm/m/it/10 able_training_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
-

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\T1/ptm/m/it/10 mary_training_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 p
ri-

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\T1/ptm/m/it/10 mary_training_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri
-

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\T1/ptm/m/it/10 mary_training_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pr
i-

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\T1/ptm/m/it/10 gap_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_row_count
=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dis-able_hold
out=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-

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\T1/ptm/m/it/10 out_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-out_du
ration=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 hold-out_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 hold-ou
t_end_date=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_backtests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 back-
tests=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 to-

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\T1/ptm/m/it/10 tal_row_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use_time_ser
ies=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 de-fault_to_known_in_advance=False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 de-fault_to_do_not_derive=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 fea-

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10 fea-

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 fea-

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\T1/ptm/m/it/10 ture_settings=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_wi
ndow_start=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fore-cast_window_end=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 win-

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\T1/ptm/m/it/10 dows_basis_unit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 treat_as_e
xponential=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 dif-fer-enc-ing_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pe-
ri-od-ic-i-

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\T1/ptm/m/it/10 ties=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mul-ti-series_id_colu
mns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_known_in_advance_features=0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 num-ber_of_do_not_derive_features=0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_cross_series_features=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ag-gre-ga-tion_type=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cross_series_group_by_columns=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 cal-en-dar_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 cal-en-

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\T1/ptm/m/it/10 dar_name=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_splits=None
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 al-

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\T1/ptm/m/it/10 low_partial_history_time_series_predictions=False\T1/ptm/m/n/10
 ,
[481] [482]
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[]\T1/ptm/m/n/10 [str] one of \T1/pcr/m/n/10 datarobot.enums.

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\T1/pcr/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/ptm/m/n/10 . Whether
 mod-els cre-ated
[483]
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[]\T1/ptm/m/n/10 [list of Pe-ri-od-ic-ity, op-tional] (New in ver-sion v2.9) a 
list of
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[]\T1/ptm/m/n/10 (New in ver-sion v2.24)
[485]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 spec: datarob
ot.helpers.partitioning_methods.DatetimePartitioningSpecification\T1/ptm/m/n/10
 ,
[486] [487]
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[]\T1/ptm/m/it/10 use_holdout_start_end_format: bool = False\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 use_backtest_start_end_format: bool = False\T1/pcr/m/n/10 ) $\O
MS/cmsy/m/n/10 !$

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 index: Op-tional[int]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 avail-

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\T1/ptm/m/it/10 able_training_start_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 avail-

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\T1/ptm/m/it/10 able_training_duration:

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\T1/ptm/m/it/10 able_training_row_count:

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\T1/ptm/m/it/10 able_training_end_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri-

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\T1/ptm/m/it/10 mary_training_start_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pri-

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\T1/ptm/m/it/10 mary_training_duration:

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\T1/ptm/m/it/10 mary_training_row_count:

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\T1/ptm/m/it/10 mary_training_end_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_start_date:

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 gap_duration:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_row_count: Op-

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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 gap_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime.datetime] =

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\T1/ptm/m/it/10 Op-tional[datetime.datetime]

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_row_count:

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\T1/ptm/m/it/10 Op-tional[int] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 val-i-da-tion_end_date: Op-

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\T1/ptm/m/it/10 tional[datetime.datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 to-tal_row_count:
[488] [489]
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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 validation_duration\T1/ptm/m/n/10 ). If \T1/p
cr/m/n/10 True\T1/ptm/m/n/10 , will use a start/end date ap-

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\T1/ptm/m/n/10 proach for spec-i-fy-ing back-tests (\T1/pcr/m/n/10 primary_trai
ning_start_date\T1/ptm/m/n/10 ,

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\T1/pcr/m/n/10 primary_training_end_date\T1/ptm/m/n/10 , \T1/pcr/m/n/10 validat
ion_start_date\T1/ptm/m/n/10 ,
[490]
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\T1/ptm/m/it/10 0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 months:

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\T1/ptm/m/it/10 int = 0\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 min-utes:

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\T1/ptm/m/it/10 true_negative_value: Op-tional[float] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 false_positive_value: Op-tional[float] = None\T1/ptm/m/n/10 ,
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 id: str\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 name: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 true_posit
ive_value: float\T1/ptm/m/n/10 ,
[493]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dict_job_id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait: int

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\T1/ptm/m/it/10 = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 pan-

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[]\T1/ptm/m/it/10 job: datarobot.models.job.Job\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/
10 !$ \T1/ptm/m/n/10 datarobot.models.predict_job.PredictJob 
[494]
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[]\T1/ptm/m/n/10 If the generic Job was not a pre-dict job, e.g. job_type !=
[495] [496]
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\T1/ptm/m/it/10 pre-dic-tions_end_date: Op-tional[datetime] =

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\T1/ptm/m/it/10 tional[List[DetectedActualValueColumn]] =
[497]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: s
tr\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.predict
ion_dataset.PredictionDataset 
[498]
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\T1/ptm/m/it/10 tion_explanations_sample=None\T1/pcr/m/n/10 ) 

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,
 \T1/ptm/m/it/10 model_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n
/10 ,

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\T1/ptm/m/it/10 max_explanations\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num_columns\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fin-

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\T1/ptm/m/it/10 ish_time\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_explanati
ons_location\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 thresh-old_low=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 thresh-old_
high=None\T1/ptm/m/n/10 ,
[499]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id\T1/ptm/m/
n/10 , \T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_explanati
ons=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 thresh-

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[]\T1/ptm/m/n/10 Compute a Pre-dic-tion-Ex-pla-na-tion-sIni-tial-iza-tion for t
he model via \T1/pcr/m/n/10 datarobot.

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[]\T1/ptm/m/n/10 Compute pre-dic-tions for the model and dataset via \T1/pcr/m/
n/10 datarobot.Model.
[500] [501] [502]
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[]\T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 off-set=None\T1/pt
m/m/n/10 , \T1/ptm/m/it/10 ex-

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[]\T1/ptm/m/it/10 row_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion_values\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion_explanations=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ad-

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\T1/ptm/m/it/10 justed_prediction=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ad-

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\T1/ptm/m/it/10 justed_prediction_values=None\T1/pcr/m/n/10 ) 
[503]
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[]\T1/ptm/m/n/10 [list] an ar-ray of dic-tio-nar-ies with a schema de-scribed a
s

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[]\T1/ptm/m/it/10 id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 count=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 vi-

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\T1/ptm/m/it/10 ous=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 next=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-

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\T1/ptm/m/it/10 dic-

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\T1/ptm/m/it/10 tion_explanations_record_location=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 just-

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\T1/ptm/m/it/10 ment_method=None\T1/pcr/m/n/10 ) 
[504]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_expla
nations_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limit=None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 off-set=0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-
[505] [506]
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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_id: 
Op-tional[str] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 in-cludes_prediction_inter
vals: Op-tional[bool]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tion_intervals_s
ize: Op-tional[int]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point: Op-tion
al[datetime] =

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\T1/ptm/m/it/10 None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_start_date: 
Op-tional[datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_end_date: 
Op-tional[datetime]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_value_column: Op
-tional[str] =
[507] [508] [509] [510] [511]
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[]\T1/ptm/m/n/10 Raised if an ob-ject passed to the \T1/pcr/m/n/10 options \T1/
ptm/m/n/10 pa-ram-e-ter is not

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\T1/ptm/m/n/10 an \T1/pcr/m/n/10 AdvancedOptions \T1/ptm/m/n/10 in-stance, a va
lid key-word ar-gu-ment from the
[512]
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[]\T1/ptm/m/it/10 sourcedata\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_name='Unti
tled Project'\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 ,
[513] [514] [515]
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[]\T1/ptm/m/it/10 clustering_project_id:

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\T1/ptm/m/it/10 str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 clus-ter-

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\T1/ptm/m/it/10 ing_model_id:

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\T1/ptm/m/it/10 max_wait: int

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\T1/ptm/m/it/10 = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 sourcedata: Union[str, pan-das.core.frame.DataFrame], tar-get
: Op-

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\T1/ptm/m/it/10 Op-tional[int] = None, met-ric: Op-tional[str] = None, au-topi-


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\T1/ptm/m/it/10 lot_on: bool = True, blueprint_threshold: Op-tional[int] = None
,

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\T1/ptm/m/it/10 re-sponse_cap: Op-tional[float] = None, par-ti-tion-ing_method:
 Op-

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\T1/ptm/m/it/10 tional[datarobot.helpers.partitioning_methods.PartitioningMetho
d] =

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\T1/ptm/m/it/10 None, pos-i-tive_class: Union[int, float, str, None] = None, ta
r-

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\T1/ptm/m/it/10 Op-tional[bool] = None, con-sider_blenders_in_recommendation: O
p-

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\T1/ptm/m/it/10 tional[bool] = None, scor-ing_code_only: Op-tional[bool] = None
,

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\T1/ptm/m/it/10 min_secondary_validation_model_count: Op-tional[int] = None,

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\T1/ptm/m/it/10 Op-tional[str] = None, au-topi-lot_with_feature_discovery: Op-

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\T1/ptm/m/it/10 tional[bool] = None, fea-ture_discovery_supervised_feature_redu
ction:

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\T1/ptm/m/it/10 Op-tional[bool] = None, un-su-per-vised_type: Op-

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\T1/ptm/m/it/10 tional[datarobot.enums.UnsupervisedTypeEnum] = None, au-topi-
[516] [517]
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[]\T1/ptm/m/n/10 Compute “All back-test”

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[]\T1/ptm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[518]
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[]\T1/ptm/m/it/10 search_params: Op-tional[Dict[str\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 str]] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/n/10 Project.list(search_params={‘project_name’: ‘red’}) >>
>
[519]
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[]\T1/ptm/m/it/10 target=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mode='quick'\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 worker
_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-

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\T1/ptm/m/it/10 i-tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ti-tion-i
ng_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/
10 ,

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\T1/ptm/m/it/10 ad-vanced_options=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait
=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\T1/ptm/m/n/10 , \T1/ptm/
m/it/10 cre-

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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_enginee
ring_prediction_point=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 un-

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\T1/ptm/m/it/10 su-per-vised_mode=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-la-t
ion-ships_configuration_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 class_mapping_aggregation_settings=None\T1/ptm/m/n/10 , \T1/ptm
/m/it/10 seg-men-ta-tion_task_id=None\T1/ptm/m/n/10 ,
[520]
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 advanced_options\T1/ptm/m/n/10 , \T1/
pcr/m/n/10 partitioning_method \T1/ptm/m/n/10 or

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[]\T1/ptm/m/it/10 target=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mode='quick'\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 met-ric=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 worker
_count=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pos-i-

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\T1/ptm/m/it/10 tive_class=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 par-ti-tion-ing
_method=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10
 , \T1/ptm/m/it/10 ad-

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\T1/ptm/m/it/10 vanced_options=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait=60
0\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-get_type=None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 cre-den-tials=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 fea-ture_engineering_prediction_point=None\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 un-su-per-vised_mode=False\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-la-ti
on-
[521] [522]
Underfull \hbox (badness 10000) in paragraph at lines 44436--44439
[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 advanced_options\T1/ptm/m/n/10 , \T1/
pcr/m/n/10 partitioning_method \T1/ptm/m/n/10 or

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[]\T1/ptm/m/it/10 order_by: Union[str, List[str], None] = None, search_params: 
Op-

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\T1/ptm/m/it/10 tional[Dict[str, Any]] = None, with_metric: Op-tional[str] = No
ne\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[523] [524] [525]
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\T1/ptm/m/it/10 tions_start_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-t
ions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_filename=None\T1/ptm
/m/n/10 ,

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\T1/ptm/m/it/10 re-lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 cre-den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-
[526]
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[]\T1/ptm/m/n/10 Raised if \T1/pcr/m/n/10 forecast_point \T1/ptm/m/n/10 or \T1/
pcr/m/n/10 predictions_start_date \T1/ptm/m/n/10 and

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[]\T1/ptm/m/it/10 data_source_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 user-name\T1/p
tm/m/n/10 , \T1/ptm/m/it/10 pass-word\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
ne\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/it/10 lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1/pt
m/m/it/10 cre-

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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 ,
[527]
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[]\T1/ptm/m/it/10 dataset_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 dataset_version_id
=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait=600\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fore-cast_point=No
ne\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-

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\T1/ptm/m/it/10 lax_known_in_advance_features_check=None\T1/ptm/m/n/10 , \T1/pt
m/m/it/10 cre-

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\T1/ptm/m/it/10 den-tials=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-tions_st
art_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pre-dic-

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\T1/ptm/m/it/10 tions_end_date=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ac-tual_val
ue_column=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sec-
[528]
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[]\T1/ptm/m/it/10 batch_size: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$
[529]
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[]\T1/ptm/m/it/10 batch_size: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.restore
_discarded_features.DiscardedFeaturesInfo 
[530]
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[]\T1/ptm/m/it/10 features: List[str], max_wait: int = 600\T1/pcr/m/n/10 ) $\OM
S/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.restore_discarded_features.FeatureRestorationSt
atus 

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait:
 int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[531]
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\T1/ptm/m/it/10 start-ing_featurelist: Op-tional[datarobot.models.featurelist.F
eaturelist]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start-ing_featurelist_id
: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 start-
[532]
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[]\T1/ptm/m/it/10 name: str, fea-tures: List[str],

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\T1/ptm/m/it/10 skip_datetime_partition_column: bool = False\T1/pcr/m/n/10 ) $\
OMS/cmsy/m/n/10 !$
[533] [534]
Underfull \hbox (badness 10000) in paragraph at lines 45544--45544
\T1/ptm/m/it/10 bool = True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 con-sider_blenders_
in_recommendation: bool = False\T1/ptm/m/n/10 ,
[535]
Underfull \hbox (badness 10000) in paragraph at lines 45614--45614
[]\T1/ptm/m/it/10 trainable\T1/ptm/m/n/10 , \T1/ptm/m/it/10 sam-ple_pct=None\T1
/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it
/10 source_project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 scor-

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\T1/ptm/m/it/10 ing_type=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_cou
nt=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-tonic_increasing_featurelist_id=<o
bject
[536]
Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
[]\T1/ptm/m/it/10 blueprint_id\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-turelist_id=
None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 train-ing_row_count=None\T1/ptm/m/n/10 ,

Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
\T1/ptm/m/it/10 train-ing_duration=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 source_
project_id=None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
\T1/ptm/m/it/10 tonic_increasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 mono-

Underfull \hbox (badness 10000) in paragraph at lines 45732--45732
\T1/ptm/m/it/10 tonic_decreasing_featurelist_id=<object ob-ject>\T1/ptm/m/n/10 
, \T1/ptm/m/it/10 use_project_settings=False\T1/ptm/m/n/10 ,

Underfull \hbox (badness 6493) in paragraph at lines 45737--45741
[]\T1/ptm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
 such as those re-turned by the
[537]
Underfull \hbox (badness 5260) in paragraph at lines 45750--45753
\T1/ptm/m/n/10 used to train the model. If spec-i-fied, nei-ther \T1/pcr/m/n/10
 training_duration \T1/ptm/m/n/10 nor

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[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.
[538]
Underfull \hbox (badness 10000) in paragraph at lines 45847--45847
[]\T1/ptm/m/it/10 model_ids: List[str], blender_method: str\T1/pcr/m/n/10 ) $\O
MS/cmsy/m/n/10 !$

Underfull \hbox (badness 10000) in paragraph at lines 45855--45858
[]\T1/ptm/m/n/10 [str] Cho-sen blend method, one from \T1/pcr/m/n/10 datarobot.
enums.

Underfull \hbox (badness 10000) in paragraph at lines 45903--45906
[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
[539]
Underfull \hbox (badness 10000) in paragraph at lines 46021--46021
[]\T1/ptm/m/it/10 status: Op-tional[datarobot.enums.Enum] = None\T1/pcr/m/n/10 
) $\OMS/cmsy/m/n/10 !$

Underfull \hbox (badness 10000) in paragraph at lines 46025--46028
[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

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[]\T1/ptm/m/it/10 status: Op-tional[datarobot.enums.Enum] = None\T1/pcr/m/n/10 
) $\OMS/cmsy/m/n/10 !$

Underfull \hbox (badness 10000) in paragraph at lines 46057--46060
[]\T1/ptm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
[540]
Underfull \hbox (badness 10000) in paragraph at lines 46099--46104
[]\T1/ptm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
[541]
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[]\T1/ptm/m/it/10 advanced_options: Op-tional[datarobot.helpers.AdvancedOptions
] =
[542]
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[]\T1/ptm/m/n/10 (New in ver-sion v2.23) one of

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\T1/pcr/m/n/10 datarobot.enums.DATETIME_AUTOPILOT_DATA_SAMPLING_METHOD\T1/ptm/m
/n/10 .

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[]\T1/ptm/m/n/10 Time Se-ries only.
[543]
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\T1/pcr/m/n/10 predictionBalance\T1/ptm/m/n/10 , \T1/pcr/m/n/10 trueFavorableAn
dUnfavorableRateParity \T1/ptm/m/n/10 or

Overfull \hbox (4.03096pt too wide) in paragraph at lines 46359--46366
[]\T1/ptm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e

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[]\T1/ptm/m/it/10 cv_method: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it
/10 val-i-da-tion_type: Op-

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\T1/ptm/m/it/10 tional[datarobot.helpers.partitioning_methods.PartitioningMetho
d]
[544]
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[]\T1/ptm/m/n/10 [PartitioningMethod, op-tional] An in-stance of \T1/pcr/m/n/10
 datarobot.
[545] [546] [547]
Underfull \hbox (badness 10000) in paragraph at lines 46687--46689
[]\T1/ptm/m/n/10 If value of \T1/pcr/m/n/10 variable_type \T1/ptm/m/n/10 is not
 from \T1/pcr/m/n/10 datarobot.enums.

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[]\T1/ptm/m/it/10 new_project_name: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/p
tm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.relatio
nships_configuration.RelationshipsConfiguration 
[548]
Underfull \hbox (badness 10000) in paragraph at lines 46871--46871
[]\T1/ptm/m/it/10 catalog_version_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 tar-

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\T1/ptm/m/it/10 get: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 date-time_partitioning
:

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\T1/ptm/m/it/10 datarobot.helpers.partitioning_methods.DatetimePartitioning\T1/
ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_wait: int = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.external_baseline_validation.ExternalBaselineVa
lidationInfo 
[549] [550]
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[]\T1/ptm/m/it/10 bias_mitigation_parent_leaderboard_id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 in-clude_bias_mitigation_feature_as_predictor_variable: bool\T1
/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e

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[]\T1/ptm/m/it/10 bias_mitigation_feature_name: str\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.model.BiasMitigationFeatureInfo 

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[]\T1/ptm/m/it/10 bias_mitigation_feature_name: str\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$
[551]
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[]\T1/ptm/m/it/10 datetime_partition_spec: Op-tional[datarobot.helpers.partitio
ning_methods.DatetimePartitioningSpecification]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 **kwargs\T1/pcr/m/n/10 )
 $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.helpers.partitioning_methods.Dat
etimePartitioning 
[552]
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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 Op-tional[datarobot.help
ers.partitioning_methods.DatetimePartitioningSpecification] 

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[]\T1/ptm/m/it/10 supported: bool\T1/ptm/m/n/10 ,
[553]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 rat-ing_table
_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[554]
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[]\T1/ptm/m/it/10 project_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/
m/n/10 List[datarobot.models.recommended_model.ModelRecommendation] 

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[]\T1/ptm/m/it/10 recommended_models: List[datarobot.models.recommended_model.M
odelRecommendation],

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\T1/ptm/m/it/10 rec-om-men-da-tion_type: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 
!$ \T1/ptm/m/n/10 Op-

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\T1/ptm/m/n/10 tional[datarobot.models.recommended_model.ModelRecommendation] 

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\T1/ptm/m/it/10 neg-a-tive_class_predictions: List[float],

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\T1/ptm/m/it/10 pos-i-tive_class_predictions: List[float],
[555]
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[]\T1/ptm/m/it/10 source: str, roc_points:

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\T1/ptm/m/it/10 List[EstimatedMetric], neg-a-

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\T1/ptm/m/it/10 pos-i-tive_class_predictions:

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\T1/ptm/m/it/10 List[float], source_model_id:

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\T1/ptm/m/it/10 str, la-bel: str, kol-
[556]
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[]\T1/ptm/m/it/10 id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 pro
ject_id: Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seg-men-ta-tion_task_id:
 Op-tional[str] = None\T1/ptm/m/n/10 ,

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 com-bined_mod
el_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[557]
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[]\T1/ptm/m/it/10 sample_pct: Op-tional[float] = None\T1/ptm/m/n/10 , \T1/ptm/m
/it/10 fea-turelist_id: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 s
cor-

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\T1/ptm/m/it/10 tonic_increasing_featurelist_id: Union[str\T1/ptm/m/n/10 , \T1/
ptm/m/it/10 ob-ject\T1/ptm/m/n/10 , \T1/ptm/m/it/10 None] = <ob-ject ob-ject>\T
1/ptm/m/n/10 , \T1/ptm/m/it/10 mono-

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[]\T1/ptm/m/it/10 training_row_count: Op-tional[int] = None\T1/ptm/m/n/10 , \T1
/ptm/m/it/10 train-

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\T1/ptm/m/it/10 ing_duration: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 train-

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\T1/ptm/m/it/10 ing_start_date: Op-tional[datetime.datetime] = None\T1/ptm/m/n/
10 ,
[558] [559]
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\T1/ptm/m/it/10 Op-tional[List[str]] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use
r_defined_segment_id_columns: Op-

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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seg-men-ta-ti
on_task_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[560]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 List[datarobot.models.segm
entation.SegmentInfo] 

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\T1/ptm/m/it/10 bool], data: Seg-men-ta-tion-
[561]
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\T1/ptm/m/it/10 Op-tional[List[str]] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 use
r_defined_segment_id_columns: Op-
[562]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 seg-men-ta-ti
on_task_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 count: int, shap_impacts: List[ShapImpactType],
[563]
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[]\T1/ptm/m/n/10 Currently [][]\T1/pcr/m/sl/10 DataStores[][]\T1/ptm/m/n/10 , [
][]\T1/pcr/m/sl/10 DataSources[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 Datasets
[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 Projects[][] \T1/ptm/m/n/10 (new in ve
r-sion v2.15) and

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[]\T1/ptm/m/it/10 client\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 path\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 limit=None\T1/pcr/m/n/10 ) 
[564]
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[]\T1/ptm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
[565]
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[]\T1/ptm/m/it/10 project_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 pre-dic-tion_id\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_id=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 data_subset=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 ex-pla-na-

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\T1/ptm/m/it/10 tion_algorithm=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 max_explanations=None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 shap_warnings=None\T1/pcr/m/n/10 ) 
[566] [567] [568]
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\T1/ptm/m/it/10 hex_column_name_lookup: Op-

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\T1/ptm/m/it/10 tional[List[UserBlueprintsHexColumnNameLookupEntryDict]]
[569]
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[]

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[]

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[570]
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[]\T1/ptm/m/it/10 user_blueprint_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 projec
t_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 blueprint\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_type: Op-tion
al[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 save_to_c
atalog: bool = True\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 custom_task_version_id:

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\T1/ptm/m/it/10 str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 save_to_catalog: bool

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\T1/ptm/m/it/10 = True\T1/ptm/m/n/10 , \T1/ptm/m/it/10 de-scrip-tion: Op-

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\T1/ptm/m/it/10 tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprint 
[571]
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[]\T1/ptm/m/it/10 blueprint_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id:
 str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 model_type: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 save_to_catalog: bool = True\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 
!$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprint 

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[]\T1/ptm/m/it/10 user_blueprint_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_
type: Op-

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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id: 
Op-tional[str]

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\T1/ptm/m/it/10 = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 save_to_catalog: bool = 
True\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[572]
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[]\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 datarobot.models.user_bl
ueprints.models.UserBlueprintAvailableInput 

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprintAddToProjec
tMenu 
[573]
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[]\T1/ptm/m/it/10 project_id: Op-tional[str] = None\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 user_blueprint_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/
cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprintAvailableTa
sks 

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[]\T1/ptm/m/it/10 output_method: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 task_code:


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\T1/ptm/m/it/10 str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 task_parameters\T1/ptm/m/n/
10 , \T1/ptm/m/it/10 project_id:

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprintValidateTas
kParameters 

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[]

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[]\T1/ptm/m/n/10 A

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\T1/ptm/m/n/10 List[datarobot.models.user_blueprints.models.UserBlueprintShared
RolesResponseValidator] 
[574]
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[]

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[]\T1/ptm/m/it/10 blueprint\T1/ptm/m/n/10 , \T1/ptm/m/it/10 project_id: Op-tion
al[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 List[datarobot.models.user_blueprints.models.VertexContextItem] 


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[]\T1/ptm/m/it/10 user_blueprint_id: str, roles:

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\T1/ptm/m/it/10 List[Union[datarobot.models.user_blueprints.models.GrantAccessC
ontrolWithUsernameValidator,

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\T1/ptm/m/it/10 datarobot.models.user_blueprints.models.GrantAccessControlWithI
dValidator]]\T1/pcr/m/n/10 )

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[]
[575]
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[]\T1/ptm/m/it/10 search: Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10
 tag: Op-tional[str] =

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\T1/ptm/m/it/10 Op-tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 owner_use
rname: Op-

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\T1/ptm/m/it/10 tional[str] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 or-der_by: s
tr = '-created'\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 datarobot.models.user_blueprints.models.UserBlueprintCatalogSear
ch 
[576]
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[]\T1/ptm/m/it/10 project_id: str, fea-ture_name: str, tar-get_value: Union[str
, int,

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\T1/ptm/m/it/10 float, List[str], None] = None, tar-get_bin_start: Union[float,
 int,

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\T1/ptm/m/it/10 None] = None, tar-get_bin_end: Union[float, int, None] = None,

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\T1/ptm/m/it/10 off-set: Op-tional[int] = None, limit: Op-tional[int] = None\T1
/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 image_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 row_count: int\
T1/ptm/m/n/10 ,
[577]
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\T1/ptm/m/it/10 po-si-tion_y: float, im-age_id: str,
[578]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_name: str\T1/pcr/m/n/10 ) $\OMS/cmsy/
m/n/10 !$

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[]\T1/ptm/m/it/10 feature_name: str, ac-ti-va-

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\T1/ptm/m/it/10 int, float, List[str], None] =

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\T1/ptm/m/it/10 None, pre-dicted_target_value:

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\T1/ptm/m/it/10 Union[str, int, float, List[str],

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\T1/ptm/m/it/10 None] = None, tar-get_values:
[579]
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[]\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 model_id: str
\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_name: str\T1/ptm/m/n/10 , \T1/ptm/m/i
t/10 off-

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\T1/ptm/m/it/10 set: Op-tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 limi
t: Op-tional[int] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 name: str\T1/ptm/m/n/
10 ,

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\T1/ptm/m/it/10 project_id: str\T1/ptm/m/n/10 ,

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\T1/ptm/m/it/10 min_transformation_probability:

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[596] [597]
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[]\T1/ptm/m/it/10 model_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 fea-ture_name: 
str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 doc-u-ment_index: Op-

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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 doc-u-ment_t
ask: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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[]\T1/ptm/m/it/10 deployment_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/p
tm/m/n/10 List[datarobot._experimental.models.retraining.RetrainingPolicy] 
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[]\T1/ptm/m/it/10 deployment_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 re-train-i
ng_policy_id: str\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$
[600]
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\T1/ptm/m/it/10 tional[int] = None\T1/ptm/m/n/10 , \T1/ptm/m/it/10 max_wait: in
t = 600\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$ \T1/ptm/m/n/10 pan-
[601]
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[]\T1/ptm/m/it/10 featurelist_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 query_fil
e_path: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 num-
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m/n/10 List[datarobot._experimental.models.data_matching.DataMatchingQuery] 

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[]\T1/ptm/m/it/10 model_id: str\T1/ptm/m/n/10 , \T1/ptm/m/it/10 ex-per-i-ment_c
ontainer_id: Op-tional[str] = None\T1/pcr/m/n/10 ) $\OMS/cmsy/m/n/10 !$

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\T1/ptm/m/n/10 Retrieve lin-eage in-for-ma-tion about a trained model. If you p
ass the op-tional
[603] [604]
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[]

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[]\T1/ptm/m/n/10 Added new meth-ods [][]\T1/pcr/m/sl/10 DataEngineQueryGenerato
r.prepare_prediction_dataset[][] \T1/ptm/m/n/10 and

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[][]\T1/pcr/m/sl/10 DataEngineQueryGenerator.prepare_prediction_dataset_from_ca
talog[][] \T1/ptm/m/n/10 that ap-ply
[605]
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[]\T1/ptm/m/n/10 Update the doc-u-men-ta-tion to sug-gest that set-ting \T1/ptm
/m/it/10 use_backtest_start_end_format \T1/ptm/m/n/10 of
[606]
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[]\T1/ptm/m/n/10 Deprecated method [][]\T1/pcr/m/sl/10 Project.open_leaderboard
_browser[][]\T1/ptm/m/n/10 . Please use [][]\T1/pcr/m/sl/10 Project.
[607]
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[]\T1/ptm/m/n/10 Added new con-ve-nience meth-ods to [][]\T1/pcr/m/sl/10 dataro
bot.models.Project[][] \T1/ptm/m/n/10 for deal-ing with par-ti-tion
[608]
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[]\T1/ptm/m/n/10 Added sup-port for pass-ing in a cre-den-tials ID or cre-den-t
ials data to [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added two new prop-er-ties to [][]\T1/pcr/m/sl/10 datarobot.mo
dels.Project[][]\T1/ptm/m/n/10 : - \T1/pcr/m/n/10 catalog_id \T1/ptm/m/n/10 -

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[]\T1/ptm/m/n/10 Added sup-port for cre-at-ing mono-tonic fea-ture lists for ti
me se-ries projects. Set

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[]\T1/ptm/m/n/10 Added in-for-ma-tion about ver-tex to ad-vanced tun-ing pa-ram
-e-ters [][]\T1/pcr/m/sl/10 datarobot.models.Model.

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[]\T1/ptm/m/n/10 Fixed is-sue with por-tions of the pay-load not us-ing camel-C
as-ing in [][]\T1/pcr/m/sl/10 Project.
[609]
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[][][]\T1/pcr/m/sl/10 CustomModelVersion[][]\T1/ptm/m/n/10 ’s \T1/pcr/m/n/10 
required_metadata \T1/ptm/m/n/10 pa-ram-e-ter has been re-moved. Use

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[][][]\T1/pcr/m/sl/10 CustomTaskVersion[][]\T1/ptm/m/n/10 ’s \T1/pcr/m/n/10 r
equired_metadata \T1/ptm/m/n/10 pa-ram-e-ter has been re-moved. Use

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[]\T1/ptm/m/n/10 The class \T1/pcr/m/n/10 CustomInferenceImage \T1/ptm/m/n/10 w
as re-moved. Use [][]\T1/pcr/m/sl/10 CustomModelVersion[][] \T1/ptm/m/n/10 with

[610]
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[]\T1/ptm/m/n/10 Added ex-per-i-men-tal sup-port to re-trieve doc-u-ment text e
x-trac-tion sam-ples us-ing: -

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[][]\T1/pcr/m/sl/10 DocumentTextExtractionSample[][] \T1/ptm/m/n/10 - [][]\T1/p
cr/m/sl/10 DocumentTextExtractionSamplePage[][] \T1/ptm/m/n/10 -

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[]\T1/ptm/m/n/10 Added sup-port to pass cal-cu-la-tion mode to pre-dic-tion ex-
pla-na-tions (\T1/ptm/m/it/10 mode \T1/ptm/m/n/10 pa-ram-e-ter in

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[][]\T1/pcr/m/sl/10 PredictionExplanations.create[][]\T1/ptm/m/n/10 ) as well a
s batch scor-ing (\T1/ptm/m/it/10 ex-pla-na-tions_mode \T1/ptm/m/n/10 in
[611]
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[]\T1/ptm/m/n/10 Added sup-port for bias mit-i-ga-tion with the fol-low-ing met
h-ods - [][]\T1/pcr/m/sl/10 Project.

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\T1/pcr/m/sl/10 get_bias_mitigated_models[][] \T1/ptm/m/n/10 - [][]\T1/pcr/m/sl
/10 Project.apply_bias_mitigation[][]

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\T1/ptm/m/n/10 - [][]\T1/pcr/m/sl/10 Project.request_bias_mitigation_feature_in
fo[][] \T1/ptm/m/n/10 - [][]\T1/pcr/m/sl/10 Project.

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\T1/pcr/m/sl/10 get_bias_mitigation_feature_info[][] \T1/ptm/m/n/10 and by addi
ng new bias mit-i-ga-

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\T1/ptm/m/n/10 tion params - bias_mitigation_feature_name - bias_mitigation_tec
hnique - in-
[612]
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[]\T1/ptm/m/n/10 Update the doc-u-men-ta-tion to sug-gest that \T1/ptm/m/it/10 
fea-ture_derivation_window_end \T1/ptm/m/n/10 of [][]\T1/pcr/m/sl/10 datarobot.


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[]\T1/ptm/m/n/10 Added new pa-ram-e-ter \T1/ptm/m/it/10 up-load_read_timeout \T
1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 BatchPredictionJob.score[][] \T1/ptm/m/n/10
 and

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[]\T1/ptm/m/n/10 Added the abil-ity to turn off su-per-vised fea-ture re-duc-ti
on for Time Se-ries projects. Op-tion
[613] [614]
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[]

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[]\T1/ptm/m/n/10 Added the abil-ity to re-trieve de-tected er-rors in the po-te
n-tially mul-ti-cat-e-gor-i-cal fea-

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\T1/ptm/m/n/10 ture types that pre-vented the fea-ture to be iden-ti-fied as mu
l-ti-cat-e-gor-i-cal. [][]\T1/pcr/m/sl/10 Project.
[615]
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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.

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[][][]\T1/pcr/m/sl/10 datarobot.models.restore_discarded_features.DiscardedFeat
uresInfo.

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[]\T1/ptm/m/n/10 Added the abil-ity to con-trol class map-ping ag-gre-ga-tion i
n mul-ti-class projects via

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[]\T1/ptm/m/n/10 Added the abil-ity to com-pute and re-trieve Fea-ture Ef-fects
 for a Mul-ti-class model us-ing

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[][]\T1/pcr/m/sl/10 datarobot.models.Model.request_feature_effects_multiclass()
[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.Model.get_feature_effects_multiclass()[][] \T1/ptm/m/n/1
0 or [][]\T1/pcr/m/sl/10 datarobot.models.Model.

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ss()[][] \T1/ptm/m/n/10 or

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[][]\T1/pcr/m/sl/10 datarobot.models.DatetimeModel.get_or_request_feature_effec
ts_multiclass()[][]
[616]
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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.get_predictions_by_forecast_d
ate_settings()[][] 

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[][][]\T1/pcr/m/sl/10 datarobot.models.Deployment.update_predictions_by_forecas
t_date_settings()[][] 
[617]
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[]\T1/ptm/m/n/10 Added the abil-ity to re-quest a spe-cific model be pre-pared 
for de-ploy-ment us-ing [][]\T1/pcr/m/sl/10 Project.
[618] [619]
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[]\T1/ptm/m/n/10 Try to read Fea-ture Im-pact re-sult if ex-ist-ing jo-bId is N
one in [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Updated [][]\T1/pcr/m/sl/10 project.start_autopilot[][] \T1/pt
m/m/n/10 to ac-cept the fol-low-ing new GA pa-ram-e-ters

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\T1/ptm/m/n/10 that are al-ready in the pub-lic API: \T1/pcr/m/n/10 consider_bl
enders_in_recommendation\T1/ptm/m/n/10 ,
[620] [621]
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[]\T1/ptm/m/n/10 Added new helper func-tion to projects to re-trieve the rec-om
-mended model. [][]\T1/pcr/m/sl/10 Project.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 docker_context_size \T1/ptm/m/n/10 and \T
1/pcr/m/n/10 docker_image_size \T1/ptm/m/n/10 to [][]\T1/pcr/m/sl/10 datarobot.


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[]\T1/ptm/m/n/10 Arguments \T1/pcr/m/n/10 dataset_definitions \T1/ptm/m/n/10 an
d \T1/pcr/m/n/10 relationships \T1/ptm/m/n/10 now ac-cept [][]\T1/pcr/m/sl/10 D
atasetDefinition[][]
[622]
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\T1/pcr/m/n/10 allow_partial_history_time_series_predictions \T1/ptm/m/n/10 at-
tribute of the [][]\T1/pcr/m/sl/10 datarobot.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve per-class lift chart data for mul-ti
-l-abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Add meth-ods to re-trieve la-bel-wise ROC curves for mul-ti-l-
abel mod-els: [][]\T1/pcr/m/sl/10 Model.

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\T1/pcr/m/sl/10 get_pairwise_correlations[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/
10 Feature.get_pairwise_joint_probabilities[][] \T1/ptm/m/n/10 and

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[]

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[]\T1/ptm/m/n/10 Support for down-load-ing fea-ture dis-cov-ery train-ing or pr
e-dic-tion dataset us-ing [][]\T1/pcr/m/sl/10 Project.
[623]
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[]\T1/ptm/m/n/10 Added [][]\T1/pcr/m/sl/10 datarobot.models.FeatureAssociationM
atrix[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 datarobot.

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\T1/pcr/m/sl/10 models.FeatureAssociationMatrixDetails[][] \T1/ptm/m/n/10 and [
][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Remove \T1/pcr/m/n/10 desired_memory \T1/ptm/m/n/10 param from
 the fol-low-ing meth-ods: [][]\T1/pcr/m/sl/10 CustomInferenceModel.

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\T1/pcr/m/sl/10 create[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersi
on.create_clean[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 CustomModelVersion.crea
te_clean[][]\T1/ptm/m/n/10 ,
[624]
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[]\T1/ptm/m/n/10 All per-class lift chart data is now avail-able for mul-ti-cla
ss mod-els us-ing [][]\T1/pcr/m/sl/10 Model.

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\T1/ptm/m/n/10 class can now use \T1/pcr/m/n/10 datarobot.TARGET_TYPE.ANOMALY \
T1/ptm/m/n/10 for its \T1/pcr/m/n/10 target_type \T1/ptm/m/n/10 pa-ram-e-ter.

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[]

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[]\T1/ptm/m/n/10 Add sup-port for start-ing and re-triev-ing Fea-ture Im-pact i
n-for-ma-tion for [][]\T1/pcr/m/sl/10 datarobot.

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[]

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[]\T1/ptm/m/n/10 Actions ap-plied to in-put data dur-ing au-to-mated fea-ture d
is-cov-ery can now be re-trieved us-ing

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[]

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[625]
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[]\T1/ptm/m/n/10 New \T1/pcr/m/n/10 sampling_method \T1/ptm/m/n/10 param in [][
]\T1/pcr/m/sl/10 Model.train_datetime[][]\T1/ptm/m/n/10 , [][]\T1/pcr/m/sl/10 P
roject.train_datetime[][]\T1/ptm/m/n/10 ,

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[]

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\T1/pcr/m/n/10 featurelist_id\T1/ptm/m/n/10 , \T1/pcr/m/n/10 credentials_ids\T1
/ptm/m/n/10 , \T1/pcr/m/n/10 project_version \T1/ptm/m/n/10 and \T1/pcr/m/n/10 
is_default \T1/ptm/m/n/10 in

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[][][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.

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[]\T1/ptm/m/n/10 Class \T1/pcr/m/n/10 FeatureEngineeringGraph \T1/ptm/m/n/10 ha
s been re-moved. Use [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 for

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[]\T1/ptm/m/n/10 for [][]\T1/pcr/m/sl/10 datarobot.models.

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[][][]\T1/pcr/m/sl/10 datarobot.
[626]
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[]\T1/ptm/m/n/10 Added new ar-gu-ment \T1/pcr/m/n/10 base_environment_id \T1/pt
m/m/n/10 to meth-ods [][]\T1/pcr/m/sl/10 CustomModelVersion.

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[]\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 base_environment_id \T1/ptm/m/n/10 a
nd \T1/pcr/m/n/10 dependencies \T1/ptm/m/n/10 to class [][]\T1/pcr/m/sl/10 data
robot.

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[]\T1/ptm/m/n/10 Added a new method to trig-ger Fea-ture Im-pact cal-cu-la-tion
 for a Cus-tom In-fer-ence Im-age:

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[]\T1/ptm/m/n/10 Added new pa-ram-e-ter \T1/ptm/m/it/10 met-ric \T1/ptm/m/n/10 
to [][]\T1/pcr/m/sl/10 datarobot.models.TargetDrift[][]\T1/ptm/m/n/10 , [][]\T1
/pcr/m/sl/10 datarobot.models.
[627]
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[]\T1/ptm/m/n/10 The de-fault value for the \T1/pcr/m/n/10 mode \T1/ptm/m/n/10 
param in [][]\T1/pcr/m/sl/10 Project.set_target[][] \T1/ptm/m/n/10 has been cha
nged from
[628]
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[]\T1/ptm/m/n/10 Added new ar-gu-ments \T1/pcr/m/n/10 explanation_algorithm \T1
/ptm/m/n/10 and \T1/pcr/m/n/10 max_explanations \T1/ptm/m/n/10 to method

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[][]\T1/pcr/m/sl/10 Model.request_training_predictions[][]\T1/ptm/m/n/10 . New 
fields \T1/pcr/m/n/10 explanation_algorithm\T1/ptm/m/n/10 ,

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\T1/ptm/m/n/10 New fields \T1/pcr/m/n/10 prediction_explanations \T1/ptm/m/n/10
 and \T1/pcr/m/n/10 shap_metadata \T1/ptm/m/n/10 have been added to class

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[][]\T1/pcr/m/sl/10 TrainingPredictionsIterator[][] \T1/ptm/m/n/10 that is re-t
urned by method [][]\T1/pcr/m/sl/10 TrainingPredictions.

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\T1/pcr/m/sl/10 request_predictions[][]\T1/ptm/m/n/10 . New fields \T1/pcr/m/n/
10 explanation_algorithm\T1/ptm/m/n/10 , \T1/pcr/m/n/10 max_explanations

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\T1/ptm/m/n/10 and \T1/pcr/m/n/10 shap_warnings \T1/ptm/m/n/10 have been added 
to class [][]\T1/pcr/m/sl/10 Predictions[][]\T1/ptm/m/n/10 . Method [][]\T1/pcr
/m/sl/10 Predictions.

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[]\T1/ptm/m/n/10 Added \T1/pcr/m/n/10 model_splits \T1/ptm/m/n/10 to [][]\T1/pc
r/m/sl/10 DatetimePartitioningSpecification[][] \T1/ptm/m/n/10 and to

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[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Model.request_predictions[][] \T1/ptm/m/n/10 now ac-cept
[629]
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[]\T1/ptm/m/n/10 New warn-ing is added to \T1/pcr/m/n/10 data_quality_warnings 
\T1/ptm/m/n/10 of [][]\T1/pcr/m/sl/10 datarobot.models.

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[]\T1/ptm/m/n/10 Scores and in-sights on ex-ter-nal test sets can be re-trieved
 us-ing [][]\T1/pcr/m/sl/10 ExternalScores[][]\T1/ptm/m/n/10 ,

Underfull \hbox (badness 10000) in paragraph at lines 54014--54017
[]\T1/ptm/m/n/10 Deployments now sup-port pre-dic-tions data col-lec-tion that 
en-ables pre-dic-tion re-

Underfull \hbox (badness 10000) in paragraph at lines 54014--54017
\T1/ptm/m/n/10 quests and re-sults to be saved in Pre-dic-tions Data Stor-age. 
See [][]\T1/pcr/m/sl/10 Deployment.

Underfull \hbox (badness 10000) in paragraph at lines 54014--54017
\T1/pcr/m/sl/10 get_predictions_data_collection_settings[][] \T1/ptm/m/n/10 and
 [][]\T1/pcr/m/sl/10 Deployment.

Underfull \hbox (badness 5022) in paragraph at lines 54062--54065
[]\T1/ptm/m/n/10 Batch Pre-dic-tion jobs now sup-port scor-ing to Azure and Goo
gle Cloud Stor-age with meth-ods

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[]

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[630]
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[]\T1/ptm/m/n/10 Secondary dataset con-fig-u-ra-tion re-trieve and dele-tion is
 eas-ier now though new

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[][]\T1/pcr/m/sl/10 SecondaryDatasetConfigurations.delete[][] \T1/ptm/m/n/10 so
ft deletes a Sec-ondary dataset con-fig-u-ra-tion.

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[]

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[631] [632]
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[]\T1/ptm/m/n/10 A new work-flow is avail-able for mak-ing pre-dic-tions in tim
e se-ries projects. To that end,

Underfull \hbox (badness 10000) in paragraph at lines 54319--54321
[]\T1/ptm/m/n/10 Per-class lift chart data is now avail-able for mul-ti-class m
od-els us-ing [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 A new boolean at-tribute \T1/pcr/m/n/10 unsupervised_mode \T1/
ptm/m/n/10 was added to [][]\T1/pcr/m/sl/10 datarobot.

Underfull \hbox (badness 10000) in paragraph at lines 54328--54331
\T1/pcr/m/sl/10 DatetimePartitioningSpecification[][]\T1/ptm/m/n/10 . When it i
s set to True, date-time par-

Underfull \hbox (badness 10000) in paragraph at lines 54328--54331
\T1/ptm/m/n/10 ti-tion-ing for un-su-per-vised time se-ries projects will be co
n-structed for now-cast-ing:
[633]
Underfull \hbox (badness 10000) in paragraph at lines 54397--54400
[]\T1/ptm/m/n/10 Time se-ries model de-ploy-ments now sup-port pre-dic-tion in-
ter-vals. See

Underfull \hbox (badness 10000) in paragraph at lines 54397--54400
[][]\T1/pcr/m/sl/10 Deployment.get_prediction_intervals_settings[][] \T1/ptm/m/
n/10 and [][]\T1/pcr/m/sl/10 Deployment.

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\T1/ptm/m/n/10 a new op-tional pa-ram-e-ter \T1/pcr/m/n/10 prediction_intervals
_size \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 It’s now pos-si-ble to get fea-ture as-so-ci-a-tion sta-tuse
s for fea-turelists us-ing [][]\T1/pcr/m/sl/10 Project.
[634]
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[]\T1/ptm/m/n/10 Monotonic con-straints are now sup-ported for OTV projects. To
 that end, the pa-ram-e-ters
[635]
Underfull \hbox (badness 10000) in paragraph at lines 54566--54567
[]\T1/ptm/m/n/10 The method [][]\T1/pcr/m/sl/10 Model.get_supported_capabilitie
s[][] \T1/ptm/m/n/10 now has an ex-tra field
[636] [637] [638]
Underfull \hbox (badness 10000) in paragraph at lines 54803--54806
[]\T1/ptm/m/n/10 The dataframe re-turned from [][]\T1/pcr/m/sl/10 datarobot.Pre
dictionExplanations.

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[]\T1/ptm/m/n/10 Information re-trieval for [][]\T1/pcr/m/sl/10 ROC Curve[][] \
T1/ptm/m/n/10 has been ex-tended to in-clude

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\T1/pcr/m/n/10 fraction_predicted_as_positive\T1/ptm/m/n/10 , \T1/pcr/m/n/10 fr
action_predicted_as_negative\T1/ptm/m/n/10 ,

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[][][]\T1/pcr/m/sl/10 datarobot.DatetimePartitioningSpecification[][] \T1/ptm/m
/n/10 at-tribute \T1/ptm/m/it/10 de-fault_to_a_priori\T1/ptm/m/n/10 , dep-re-
[639]
Underfull \hbox (badness 10000) in paragraph at lines 54927--54929
[]\T1/ptm/m/n/10 New class for work-ing with model com-pli-ance doc-u-men-ta-ti
on fea-ture of DataRobot: class

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[]\T1/ptm/m/n/10 Cross se-ries fea-tures can now be cre-ated within time se-rie
s mul-ti-series projects us-ing the

Underfull \hbox (badness 10000) in paragraph at lines 54942--54946
\T1/pcr/m/n/10 use_cross_series_features \T1/ptm/m/n/10 and \T1/pcr/m/n/10 aggr
egation_type \T1/ptm/m/n/10 at-tributes of the [][]\T1/pcr/m/sl/10 datarobot.
[640]
Underfull \hbox (badness 10000) in paragraph at lines 54957--54960
[][][]\T1/pcr/m/sl/10 Project.upload_dataset[][] \T1/ptm/m/n/10 and [][]\T1/pcr
/m/sl/10 Project.create[][] \T1/ptm/m/n/10 now ac-cept an op-tional pa-ram-e-te
r of

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[]\T1/ptm/m/n/10 New op-tional pa-ram-e-ter \T1/ptm/m/it/10 fall-back_to_parent
_insights \T1/ptm/m/n/10 has been added to [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Training pre-dic-tions for date-time par-ti-tioned projects no
w sup-port the new data sub-set
[641]
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[]\T1/ptm/m/n/10 New meth-ods and ob-jects, in-clud-ing [][]\T1/pcr/m/sl/10 Mod
el.start_advanced_tuning_session[][]\T1/ptm/m/n/10 ,

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[][]\T1/pcr/m/sl/10 Model.get_advanced_tuning_parameters[][]\T1/ptm/m/n/10 , []
[]\T1/pcr/m/sl/10 Model.advanced_tune[][]\T1/ptm/m/n/10 , and

Underfull \hbox (badness 10000) in paragraph at lines 55064--55066
[]\T1/ptm/m/n/10 A cus-tom pre-dic-tion thresh-old may now be con-fig-ured for 
each model via [][]\T1/pcr/m/sl/10 Model.

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[]\T1/ptm/m/n/10 Individual cross val-i-da-tion scores can be re-trieved for ne
w mod-els us-ing [][]\T1/pcr/m/sl/10 Model.
[642]
Underfull \hbox (badness 10000) in paragraph at lines 55136--55139
[]\T1/ptm/m/n/10 A new rec-om-men-da-tion type “Rec-om-mended for De-ploy-men
-t” has been added to
[643] [644]
Underfull \hbox (badness 10000) in paragraph at lines 55317--55324
[]\T1/ptm/m/n/10 Three new op-tions have been made avail-able in the [][]\T1/pc
r/m/sl/10 datarobot.
[645] [646] [647] [648] [649] [650] [651]
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[]\T1/ptm/m/n/10 its
[652] [653]
Underfull \hbox (badness 10000) in paragraph at lines 56113--56115
[]\T1/pcr/m/n/10 Project.update \T1/ptm/m/n/10 dep-re-cated in fa-vor of spe-ci
fic up-dates: \T1/pcr/m/n/10 rename\T1/ptm/m/n/10 , \T1/pcr/m/n/10 unlock_holdo
ut\T1/ptm/m/n/10 ,
[654] [655]
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[]\T1/pcr/m/n/10 recommendation_settings \T1/ptm/m/n/10 pa-ram-e-ter of \T1/pcr
/m/n/10 Project.start \T1/ptm/m/n/10 is dep-re-cated in fa-vor of
[656] [657] [658] [659] [660]
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[]\T1/pcr/m/n/10 datarobot.models.training_predictions\T1/ptm/m/n/10 ,
[661] (./datarobot-public-api-client.ind [662]
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[]\T1/pcr/m/n/10 AccuracyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotPreview \T1/ptm/m/n/10 (\T1/ptm/m/it/10 cl
ass in

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[]\T1/pcr/m/n/10 AccuracyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.Model

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.PrimeModel

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[]\T1/pcr/m/n/10 advanced_tune() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.mode
ls.RatingTableModel

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[]\T1/pcr/m/n/10 AdvancedTuningSession \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in


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[]\T1/pcr/m/n/10 analyze_and_model() \T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.
models.Project

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[]\T1/pcr/m/n/10 AnomalyAssessmentExplanations \T1/ptm/m/n/10 (\T1/ptm/m/it/10 
class in

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[]\T1/pcr/m/n/10 AnomalyAssessmentPredictionsPreview

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in datarobot.models.anomaly_assessment\T1
/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyAssessmentRecord \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class 
in

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\T1/ptm/m/it/10 datarobot.models.anomaly_assessment\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlot \T1/ptm/m/n/10 (\T1/ptm/m/it/10 class in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlotPreview \T1/ptm/m/n/10 (\T1/ptm/m/it/10 cla
ss in

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\T1/ptm/m/it/10 datarobot.models.datetime_trend_plots\T1/ptm/m/n/10 ),

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[]\T1/pcr/m/n/10 AnomalyOverTimePlotsMetadata \T1/ptm/m/n/10 (\T1/ptm/m/it/10 c
lass in

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[]\T1/pcr/m/n/10 apply_bias_mitigation()

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[]\T1/pcr/m/n/10 apply_time_series_data_prep_and_score()

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roject class

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atingTableModel

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egmentation.SegmentationTask

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tionTask class

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delJob class

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edictJob class

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ataStore class

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odels.Blueprint

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odels.Dataset

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odels.Feature

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odels.Project

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artitioning class

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tomated_documentation.AutomatedDocument

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models.model_lineage.ModelLineage
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models.retraining.RetrainingPolicy

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yGenerator class

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edictionJob class

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nce_doc_template.ComplianceDocTemplate

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odelVersionConversion

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FeatureHistogram

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AssociationFeaturelists

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AssociationMatrix

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AssociationMatrixDetails

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ImpactJob class

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tionFeature class

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_report.MissingValuesReport

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ueprintChart class

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gFeature class

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gFeaturelist class

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tegoricalHistogram

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eConditionalProbabilities

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eCorrelations class

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ableModel class

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nshipsConfiguration

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ation.SegmentationTask

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g_predictions.TrainingPredictions

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i.ImageAugmentationOptions

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anations class

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anationsInitialization

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stomTask

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taSource

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dels.Project

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s.Deployment

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odels.Dataset

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\T1/ptm/m/n/10 (\T1/ptm/m/it/10 datarobot.models.TrainingPredictionsJob
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