Read the Docs build information
Build id: 1531741
Project: datarobot-public-api-client
Version: v3.2.0b0
Commit: 6f972b2e5d14bb64485dfb36dda46a2fff6367db
Date: 2023-06-12T17:36:36.907957Z
State: finished
Success: True


[rtd-command-info] start-time: 2023-06-12T17:36:39.020385Z, end-time: 2023-06-12T17:36:49.705736Z, duration: 10, 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,192.30.255.113' (ECDSA) to the list of known hosts.

[rtd-command-info] start-time: 2023-06-12T17:36:49.919802Z, end-time: 2023-06-12T17:36:50.490575Z, duration: 0, exit-code: 0
git checkout --force origin/v3.2.0b0
Note: switching to 'origin/v3.2.0b0'.

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 6f972b2e Enable no return any check. (#2489)

[rtd-command-info] start-time: 2023-06-12T17:36:50.721899Z, end-time: 2023-06-12T17:36:50.798628Z, duration: 0, exit-code: 0
git clean -d -f -f


[rtd-command-info] start-time: 2023-06-12T17:36:52.200071Z, end-time: 2023-06-12T17:36:56.923703Z, 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/v3.2.0b0/bin/python3.7
Also creating executable in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/v3.2.0b0/bin/python
Installing setuptools, pip, wheel...
done.

[rtd-command-info] start-time: 2023-06-12T17:36:57.172363Z, end-time: 2023-06-12T17:36:58.353150Z, duration: 1, exit-code: 0
python -m pip install --upgrade --no-cache-dir pip setuptools
Requirement already satisfied: pip in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/v3.2.0b0/lib/python3.7/site-packages (23.1.2)
Requirement already satisfied: setuptools in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/v3.2.0b0/lib/python3.7/site-packages (67.8.0)

[rtd-command-info] start-time: 2023-06-12T17:36:58.715093Z, end-time: 2023-06-12T17:37:05.742918Z, duration: 7, 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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  Created wheel for mock: filename=mock-1.0.1-py3-none-any.whl size=23753 sha256=9a73140dec078c1c93bee19e44ce2f1fd1d576ea5051ef5bd145926aa002faa0
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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-2.1.3 Pygments-2.15.1 alabaster-0.7.13 babel-2.12.1 certifi-2023.5.7 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-23.1 pillow-5.4.1 pytz-2023.3 readthedocs-sphinx-ext-2.2.1 recommonmark-0.5.0 requests-2.31.0 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 urllib3-2.0.3

[rtd-command-info] start-time: 2023-06-12T17:37:05.967196Z, end-time: 2023-06-12T17:37:46.199380Z, duration: 40, 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/v3.2.0b0
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Collecting pandas>=0.15 (from datarobot==3.2.0b0)
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Building wheels for collected packages: datarobot, jupyter-contrib-nbextensions, jupyter_contrib_core
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Installing collected packages: webencodings, wcwidth, types-urllib3, types-PyYAML, types-pytz, types-python-dateutil, trafaret, ptyprocess, pickleshare, mistune, jupyter_highlight_selected_word, ipython_genutils, fastjsonschema, backcall, zipp, wrapt, websocket-client, urllib3, typing-extensions, types-requests, typed-ast, traitlets, tornado, tomlkit, tomli, tinycss2, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, soupsieve, sniffio, Send2Trash, pyzmq, pyyaml, python-dateutil, pyrsistent, pyflakes, pycparser, pycodestyle, py, psutil, prompt-toolkit, prometheus-client, pkgutil-resolve-name, Pillow, pexpect, pathspec, parso, pandocfilters, numpy, nest-asyncio, mypy-extensions, mock, mccabe, lxml, lazy-object-proxy, jupyterlab-pygments, jinja2, isort, iniconfig, exceptiongroup, entrypoints, dill, defusedxml, decorator, debugpy, coverage, bleach, terminado, platformdirs, pandas, mypy, matplotlib-inline, jupyter_core, jedi, importlib-resources, importlib-metadata, cffi, beautifulsoup4, astroid, anyio, Sphinx, responses, requests-toolbelt, pylint, pluggy, jupyter-client, ipython, flake8, click, attrs, argon2-cffi-bindings, sphinx-rtd-theme, sphinx-autodoc-typehints, pytest, numpydoc, jsonschema, ipykernel, datarobot, black, argon2-cffi, pytest-cov, pytest-asyncio, nbformat, nbclient, nbconvert, nbsphinx, jupyter-server, notebook-shim, nbclassic, notebook, jupyter_contrib_core, jupyter_nbextensions_configurator, jupyter-contrib-nbextensions
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    Uninstalling sphinx-rtd-theme-0.4.3:
      Successfully uninstalled sphinx-rtd-theme-0.4.3
Successfully installed Pillow-9.2.0 Send2Trash-1.8.2 Sphinx-4.3.2 anyio-3.7.0 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 astroid-2.13.5 attrs-23.1.0 backcall-0.2.0 beautifulsoup4-4.12.2 black-22.8.0 bleach-6.0.0 cffi-1.15.1 click-8.1.3 coverage-7.2.7 datarobot-3.2.0b0 debugpy-1.6.7 decorator-5.1.1 defusedxml-0.7.1 dill-0.3.6 entrypoints-0.4 exceptiongroup-1.1.1 fastjsonschema-2.17.1 flake8-5.0.4 importlib-metadata-4.2.0 importlib-resources-5.12.0 iniconfig-2.0.0 ipykernel-6.16.2 ipython-7.34.0 ipython_genutils-0.2.0 isort-5.10.1 jedi-0.18.2 jinja2-3.1.2 jsonschema-4.17.3 jupyter-client-7.4.9 jupyter-contrib-nbextensions-0.7.0 jupyter-server-1.24.0 jupyter_contrib_core-0.4.2 jupyter_core-4.12.0 jupyter_highlight_selected_word-0.2.0 jupyter_nbextensions_configurator-0.6.3 jupyterlab-pygments-0.2.2 lazy-object-proxy-1.9.0 lxml-4.9.2 matplotlib-inline-0.1.6 mccabe-0.7.0 mistune-2.0.5 mock-3.0.5 mypy-1.0.0 mypy-extensions-1.0.0 nbclassic-1.0.0 nbclient-0.7.4 nbconvert-7.4.0 nbformat-5.8.0 nbsphinx-0.8.9 nest-asyncio-1.5.6 notebook-6.5.4 notebook-shim-0.2.3 numpy-1.21.6 numpydoc-1.4.0 pandas-1.3.5 pandocfilters-1.5.0 parso-0.8.3 pathspec-0.11.1 pexpect-4.8.0 pickleshare-0.7.5 pkgutil-resolve-name-1.3.10 platformdirs-3.5.3 pluggy-1.0.0 prometheus-client-0.17.0 prompt-toolkit-3.0.38 psutil-5.9.5 ptyprocess-0.7.0 py-1.11.0 pycodestyle-2.9.1 pycparser-2.21 pyflakes-2.5.0 pylint-2.15.0 pyrsistent-0.19.3 pytest-7.1.2 pytest-asyncio-0.21.0 pytest-cov-4.1.0 python-dateutil-2.8.2 pyyaml-6.0 pyzmq-25.1.0 requests-toolbelt-1.0.0 responses-0.21.0 sniffio-1.3.0 soupsieve-2.4.1 sphinx-autodoc-typehints-1.17.1 sphinx-rtd-theme-1.0.0 sphinxcontrib-applehelp-1.0.2 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.0 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 terminado-0.17.1 tinycss2-1.2.1 tomli-2.0.1 tomlkit-0.11.8 tornado-6.2 trafaret-2.1.1 traitlets-5.9.0 typed-ast-1.5.4 types-PyYAML-6.0.12 types-python-dateutil-2.8.19 types-pytz-2022.2.1.0 types-requests-2.28.11 types-urllib3-1.26.25 typing-extensions-4.6.3 urllib3-1.26.16 wcwidth-0.2.6 webencodings-0.5.1 websocket-client-1.5.3 wrapt-1.15.0 zipp-3.15.0

[rtd-command-info] start-time: 2023-06-12T17:37:48.988242Z, end-time: 2023-06-12T17:37:49.085021Z, duration: 0, exit-code: 0
cat sdk_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

import sphinx_rtd_theme

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, "sdk_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",
    "sphinx_autodoc_typehints",
]

# 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.

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'

# Nitpick targets to ignore:
# These are third-party packages that don't expose documentation for sphinx to link

nitpick_ignore = [
    ("py:data", "typing.Tuple"),
    ("py:data", "typing.Optional"),
    ("py:data", "typing.Union"),
    ("py:data", "typing.NoReturn"),
    ("py:data", "typing.Any"),
    ("py:class", "pandas.core.frame.DataFrame"),
    ("py:class", "requests.models.Response"),
    ("py:class", "datetime.datetime"),
    ("py:class", "PIL.Image.Image"),
    ("py:class", "collections.OrderedDict"),
]


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:
        html_theme_path = [theme.get_html_theme_path()]

# Define websupport2_base_url and websupport2_static_url
if globals().get('websupport2_base_url', False):
    websupport2_base_url = 'https://readthedocs.com/websupport'
    websupport2_static_url = 'https://assets.readthedocs.com/'


#Add project information to the template context.
context = {
    'using_theme': using_rtd_theme,
    'html_theme': html_theme,
    'current_version': "v3.2.0b0",
    'version_slug': "v3.2.0b0",
    'MEDIA_URL': "https://media.readthedocs.com/media/",
    'STATIC_URL': "https://assets.readthedocs.com/",
    'PRODUCTION_DOMAIN': "readthedocs.com",
    'proxied_static_path': "/_/static/",
    'versions': [
    ("v3.2.0b0", "/en/v3.2.0b0/"),
    ("v3.1.1", "/en/v3.1.1/"),
    ("v3.1.0", "/en/v3.1.0/"),
    ("v3.0.3", "/en/v3.0.3/"),
    ("v3.0.2", "/en/v3.0.2/"),
    ("v3.0.1", "/en/v3.0.1/"),
    ("v2.28.1", "/en/v2.28.1/"),
    ("v2.28.0", "/en/v2.28.0/"),
    ("v2.27.3", "/en/v2.27.3/"),
    ("v2.27.2", "/en/v2.27.2/"),
    ("v2.27.1", "/en/v2.27.1/"),
    ("v2.27.0", "/en/v2.27.0/"),
    ("v2.26.2", "/en/v2.26.2/"),
    ("v2.26.1", "/en/v2.26.1/"),
    ("v2.26.0", "/en/v2.26.0/"),
    ("v2.25.1", "/en/v2.25.1/"),
    ("v2.25.0", "/en/v2.25.0/"),
    ("v2.24.0", "/en/v2.24.0/"),
    ("v2.23.1", "/en/v2.23.1/"),
    ("v2.23.0", "/en/v2.23.0/"),
    ("v2.22.2", "/en/v2.22.2/"),
    ("v2.22.1", "/en/v2.22.1/"),
    ("v2.22.0", "/en/v2.22.0/"),
    ("v2.21.6", "/en/v2.21.6/"),
    ("v2.21.5", "/en/v2.21.5/"),
    ("v2.21.4", "/en/v2.21.4/"),
    ("v2.21.3", "/en/v2.21.3/"),
    ("v2.21.2", "/en/v2.21.2/"),
    ("v2.20.0", "/en/v2.20.0/"),
    ("v2.19.0", "/en/v2.19.0/"),
    ("v2.18.0", "/en/v2.18.0/"),
    ("v2.17.0", "/en/v2.17.0/"),
    ("v2.16.0", "/en/v2.16.0/"),
    ("v2.15.1", "/en/v2.15.1/"),
    ("v2.14.2", "/en/v2.14.2/"),
    ("v2.13.3", "/en/v2.13.3/"),
    ("v2.12.2", "/en/v2.12.2/"),
    ("v2.11.2", "/en/v2.11.2/"),
    ("v2.9.3", "/en/v2.9.3/"),
    ("v2.8.3", "/en/v2.8.3/"),
    ("v2.7.3", "/en/v2.7.3/"),
    ("latest-release", "/en/latest-release/"),
    ("early-access", "/en/early-access/"),
    ],
    'downloads': [ 
    ],
    'subprojects': [ 
    ],
    'slug': 'datarobot-public-api-client',
    'name': u'public_api_client',
    'rtd_language': u'en',
    'programming_language': u'py',
    'canonical_url': 'https://datarobot-public-api-client.readthedocs-hosted.com/en/latest-release/',
    'analytics_code': 'None',
    'single_version': False,
    'conf_py_path': '/sdk_docs/',
    'api_host': 'https://readthedocs.com',
    'github_user': 'datarobot',
    'proxied_api_host': '/_',
    'github_repo': 'public_api_client',
    'github_version': 'v3.2.0b0',
    'display_github': True,
    'bitbucket_user': 'None',
    'bitbucket_repo': 'None',
    'bitbucket_version': 'v3.2.0b0',
    'display_bitbucket': False,
    'gitlab_user': 'None',
    'gitlab_repo': 'None',
    'gitlab_version': 'v3.2.0b0',
    'display_gitlab': False,
    'READTHEDOCS': True,
    'using_theme': (html_theme == "default"),
    'new_theme': (html_theme == "sphinx_rtd_theme"),
    'source_suffix': SUFFIX,
    'ad_free': False,
    'docsearch_disabled': False,
    'user_analytics_code': '',
    'global_analytics_code': 'UA-17997319-2',
    'commit': '6f972b2e',
}

# 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():
    
    for key in context:
        if key not in html_context:
            html_context[key] = context[key]
    
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 'branch' == '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/1531741/'

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',
])

if chinese:
    latex_engine = latex_engine_user or 'xelatex'

    latex_elements_rtd = {
        'preamble': '\\usepackage[UTF8]{ctex}\n',
    }
    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: 2023-06-12T17:37:49.284219Z, end-time: 2023-06-12T17:38:38.112230Z, duration: 48, exit-code: 0
python -m sphinx -T -E -b html -d _build/doctrees -D language=en . $READTHEDOCS_OUTPUT/html
Running Sphinx v4.3.2
loading translations [en]... done
making output directory... done
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WARNING: error while formatting arguments for datarobot.models.anomaly_assessment.AnomalyAssessmentRecord: __name__
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[rtd-command-info] start-time: 2023-06-12T17:38:38.437436Z, end-time: 2023-06-12T17:39:27.948432Z, duration: 49, exit-code: 0
python -m sphinx -T -E -b latex -d _build/doctrees -D language=en . $READTHEDOCS_OUTPUT/pdf
Running Sphinx v4.3.2
loading translations [en]... done
making output directory... done
[autosummary] generating autosummary for: CHANGES.rst, autodoc/api_reference.rst, examples/index.rst, getting_started.rst, index.rst, reference/admin/credentials.rst, reference/admin/index.rst, reference/admin/sharing.rst, reference/data/database_connectivity.rst, reference/data/dataset.rst, ..., reference/modeling/spec/segmented_modeling.rst, reference/modeling/spec/time_series.rst, reference/modeling/spec/unsupervised_anomaly.rst, reference/modeling/spec/unsupervised_clustering.rst, reference/modeling/spec/visualai.rst, reference/predictions/batch_predictions.rst, reference/predictions/index.rst, reference/predictions/predict_job.rst, reference/use_cases/index.rst, reference/use_cases/use_cases.rst
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Unless you specifically know better, you don't have the access to use this functionality in the app, so this code will not work.
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WARNING: error while formatting arguments for datarobot.models.anomaly_assessment.AnomalyAssessmentRecord: __name__
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[rtd-command-info] start-time: 2023-06-12T17:39:28.723874Z, end-time: 2023-06-12T17:39:28.784587Z, 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: 2023-06-12T17:39:29.083498Z, end-time: 2023-06-12T17:39:46.990240Z, duration: 17, exit-code: 0
latexmk -r latexmkrc -pdf -f -dvi- -ps- -jobname=datarobot-public-api-client -interaction=nonstopmode
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Chapter 1.
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Chapter 2.

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[]\T1/qtm/m/n/10 For mul-ti-class mod-els, you can get a list of per-class lift
 charts us-ing the \T1/txtt/m/n/10 Model \T1/qtm/m/n/10 method

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[]\T1/qtm/m/n/10 For mul-ti-class mod-els, you can get a list of per-class lift
 charts us-ing the \T1/txtt/m/n/10 Model \T1/qtm/m/n/10 method

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[]\T1/qtm/m/n/10 Just as with the lift and ROC charts, you can use \T1/txtt/m/n
/10 Model \T1/qtm/m/n/10 meth-ods \T1/txtt/m/n/10 get_residuals_chart \T1/qtm/m
/n/10 and
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[]\T1/qtm/m/n/10 gap_duration (str): A du-ra-tion string such as those re-turne
d by the [][]\T1/txtt/m/sl/10 partitioning_methods.
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\T1/qtm/m/n/10 and \T1/txtt/m/n/10 validation_start_date\T1/qtm/m/n/10 . Us-ing
 the same date for both \T1/txtt/m/n/10 primary_training_end_date \T1/qtm/m/n/1
0 and

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[]\T1/qtm/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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0 . You can also re-trieve the de-tailed meta-data us-ing
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[]\T1/qtm/m/n/10 Pre-dic-tion in-ter-vals can be cal-cu-lated for a [][]\T1/txt
t/m/sl/10 DatetimeModel[][] \T1/qtm/m/n/10 us-ing the [][]\T1/txtt/m/sl/10 Date
timeModel.
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[58] [59] [60] [61 <./sample1.png> <./sample2.png>] [62] [63 <./activation_map1
.png> <./activation_map2.png>] [64] [65 <./embeddings.png>]
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[]\T1/qtm/m/n/10 1. Up-load a pre-dic-tion dataset, spec-i-fy-ing \T1/txtt/m/n/
10 predictions_start_date\T1/qtm/m/n/10 , \T1/txtt/m/n/10 predictions_end_date\
T1/qtm/m/n/10 , and
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[]\T1/qtm/m/n/10 Up-load 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/qtm/b/n/10 Note:  \T1/txtt/m/n/10 PredictionExplanations.get_all_as_datafra
me() \T1/qtm/m/n/10 and \T1/txtt/m/n/10 PredictionExplanations.
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[]\T1/qtm/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/txtt/m/n/10 PredictJob.
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[]\T1/qtm/m/n/10 In or-der to sched-ule the ex-e-cu-tion of a Batch Pre-dic-tio
n job, a def-i-ni-tion must first be cre-ated, us-

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[]\T1/qtm/m/n/10 Job def-i-ni-tions are only be ex-e-cuted by the sched-uler if
 \T1/txtt/m/n/10 enabled \T1/qtm/m/n/10 is set to \T1/txtt/m/n/10 True\T1/qtm/m
/n/10 . If you have a

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 job, but should now be stopped, sim-ply
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\T1/qtm/m/n/10 of [][]\T1/txtt/m/sl/10 datarobot.models.deployment.DeploymentGr
antSharedRoleWithId[][] \T1/qtm/m/n/10 and [][]\T1/txtt/m/sl/10 datarobot.model
s.

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[]\T1/qtm/m/n/10 Re-fer to [][]\T1/txtt/m/sl/10 ExecutionEnvironment[][] \T1/qt
m/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/qtm/m/n/10 cus-tom_model.update(is_training_data_for_versions_permanently
_enabled=True) cus-
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[]\T1/qtm/m/n/10 Use Cases sup-port \T1/txtt/m/n/10 SHARING_ROLE.OWNER\T1/qtm/m
/n/10 , \T1/txtt/m/n/10 SHARING_ROLE.EDITOR\T1/qtm/m/n/10 , \T1/txtt/m/n/10 SHA
RING_ROLE.CONSUMER \T1/qtm/m/n/10 and
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\T1/qtm/m/n/10 cu-lat-ing fair-ness. Can be one of \T1/txtt/m/n/10 proportional
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\T1/txtt/m/n/10 predictionBalance\T1/qtm/m/n/10 , \T1/txtt/m/n/10 trueFavorable
AndUnfavorableRateParity \T1/qtm/m/n/10 or
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[]\T1/qtm/m/n/10 Fil-ter by \T1/qtm/m/it/10 sta-tus \T1/qtm/m/n/10 == Anoma-lyA
ssess-mentSta-
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[]

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

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

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[]\T1/qtm/m/n/10 [string] The URL for the scored dataset (e.g.:
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[]\T1/qtm/m/n/10 [str or None] Type of au-to-mated doc-u-ment. You can spec-ify
:
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[]

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[]\T1/qtm/m/n/10 Au-then-ti-ca-tion to-ken type: To-ken, Bearer.
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[]\T1/qtm/m/n/10 The pro-gram-ming lan-guage of the cus-tom in-fer-ence model. 
Can be

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\T1/qtm/m/n/10 ues: [\T1/qtm/m/it/10 datarobot.TARGET_TYPE.BINARY\T1/qtm/m/n/10
 , \T1/qtm/m/it/10 datarobot.TARGET_TYPE.REGRESSION\T1/qtm/m/n/10 ,

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\T1/qtm/m/it/10 datarobot.TARGET_TYPE.MULTICLASS\T1/qtm/m/n/10 , \T1/qtm/m/it/1
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\T1/qtm/m/n/10 tom in-fer-ence model. Val-ues: [\T1/qtm/m/it/10 datarobot.TARGE
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\T1/qtm/m/it/10 datarobot.TARGET_TYPE.REGRESSION\T1/qtm/m/n/10 , \T1/qtm/m/it/1
0 datarobot.TARGET_TYPE.MULTICLASS\T1/qtm/m/n/10 ,

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[]\T1/qtm/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/qtm/m/n/10 See datarobot.enums.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS
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[]\T1/qtm/m/n/10 whether anomaly pre-dic-tions should be cal-i-brated to
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[]\T1/qtm/m/n/10 the tar-get typed

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

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\T1/qtm/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/qtm/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/qtm/m/n/10 the list of a file items ids to be deleted Ex-am-ple:

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[]\T1/qtm/m/n/10 Ad-di-tional pa-ram-e-ters re-
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[]\T1/qtm/m/n/10 Ad-di-tional pa-ram-e-ters re-
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[]\T1/qtm/m/n/10 [str] the full JDBC url, for ex-am-ple \T1/qtm/m/it/10 jdbc:po
stgresql://my.dbaddress.org:5432/my_db\T1/qtm/m/n/10 . 
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[]\T1/qtm/m/n/10 [str] op-tional, the full JDBC url, for ex-am-ple
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[]\T1/qtm/m/n/10 Fil-ter avail-able
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[]\T1/qtm/m/n/10 Fil-ter avail-able
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0 datarobot.enums.

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[]\T1/qtm/m/n/10 A list of \T1/txtt/m/n/10 datarobot.enums.DATETIME_TREND_PLOTS
_RESOLUTION\T1/qtm/m/n/10 ,
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[]\T1/qtm/m/n/10 A list of \T1/txtt/m/n/10 datarobot.enums.DATETIME_TREND_PLOTS
_RESOLUTION\T1/qtm/m/n/10 ,
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[]\T1/qtm/m/n/10 A list of \T1/txtt/m/n/10 datarobot.enums.DATETIME_TREND_PLOTS
_RESOLUTION\T1/qtm/m/n/10 ,
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[]\T1/qtm/m/n/10 datarobot-key: str. Cor-re-sponds the to the \T1/txtt/m/n/10 P
redictionServer\T1/qtm/m/n/10 ’s “s-
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[]\T1/qtm/m/n/10 [bool] Is ‘’True’’ if pre-dic-tions by fore-cast

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\T1/qtm/m/n/10 date is en-abled for this de-ploy-ment. To up-date this set-ting
, see
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[]\T1/qtm/m/n/10 [list] To cre-ate or up-date ex-ist-ing seg-ment anal-y-sis at
-tributes, see
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[]\T1/qtm/m/n/10 [bool] If tar-get drift track-ing is en-abled for this de-ploy
-ment.

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tings, see

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y-ment.

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\T1/qtm/m/n/10 To cre-ate or up-date ex-ist-ing ‘’fea-ture_drift’’ set-
tings, see
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set-tings, see
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\T1/qtm/m/n/10 cre-ate or up-date ex-ist-ing ‘’pre-dic-tion_warning’’ s
et-tings, see
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[]
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�-me-di-
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[]\T1/qtm/m/n/10 His-togram in-for-ma-tion as a mul-ti-cat-e-gor-i-cal_histogra
m. The
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[]\T1/qtm/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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[345]
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[]\T1/qtm/m/n/10 [list(dict)] List of con-di-tional prob-a-bil-ity val-ues with
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\T1/txtt/m/n/10 pairwise_conditional_probabilities.statistic_dataframes[(0,1)].

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

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

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de-faults to
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[]\T1/qtm/m/n/10 List of fea-ture deriva-tion win-

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

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[]\T1/qtm/m/n/10 Clos-est value of pre-dic-
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[]\T1/qtm/m/n/10 Each el-e-ment is a \T1/txtt/m/n/10 datarobot.helpers.

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

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\T1/qtm/m/n/10 el-e-ment is a dic-tio-nary or a \T1/txtt/m/n/10 datarobot.helpe
rs.feature_discovery.
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[]\T1/qtm/m/n/10 The re-quested re-la-tion-ships

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\T1/qtm/m/n/10 el-e-ment is a dic-tio-nary or a \T1/txtt/m/n/10 datarobot.helpe
rs.feature_discovery.
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[]\T1/qtm/m/n/10 Col-umn(s) from the sec-ond
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[]\T1/qtm/m/n/10 List of fea-ture deriva-tion win-

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

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

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[]\T1/qtm/m/n/10 Clos-est value of pre-dic-
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[]\T1/qtm/m/n/10 cre-den-tials used by the sec-ondary

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[]\T1/qtm/m/n/10 list of sec-
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[]\T1/qtm/m/n/10 [list of Sec-ondary-Dataset-Con-fig-u-ra-tions] The re-
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[]\T1/qtm/m/it/10 ff_metadata_datetime_per_backtest\T1/txtt/m/n/10 )|  
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
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[]
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).
[443]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[444] [445]
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[]\T1/qtm/m/n/10 [str] which of train-ing_row_count, train-ing_duration, or tra
in-
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[]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[450] [451]
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[]\T1/qtm/m/n/10 The back-test

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[]\T1/qtm/m/n/10 The back-test

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[]\T1/qtm/m/n/10 The back-test
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[]\T1/qtm/m/n/10 [ForecastVsActualPlotPreview] a [][]\T1/txtt/m/sl/10 ForecastV
sActualPlotPreview[][] \T1/qtm/m/n/10 rep-re-sent-ing
[458] [459]
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[]\T1/qtm/m/n/10 [AnomalyOverTimePlotPreview] a [][]\T1/txtt/m/sl/10 AnomalyOve
rTimePlotPreview[][] \T1/qtm/m/n/10 rep-re-sent-ing
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[]

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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]
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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[473]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[474]
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[]

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[]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
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[]

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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[497] [498]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[499] [500]
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).

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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[504] [505] [506]
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[]\T1/qtm/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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[507] [508] [509] [510] [511] [512]
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\T1/qtm/m/it/10 low_partial_history_time_series_predictions=False\T1/qtm/m/n/10
 , 

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[]\T1/qtm/m/n/10 This is the spec-i-fi-ca-tion that should be passed to [][]\T1
/txtt/m/sl/10 Project.analyze_and_model[][] \T1/qtm/m/n/10 via the

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[]\T1/qtm/m/n/10 Note that ei-ther (\T1/txtt/m/n/10 holdout_start_date\T1/qtm/m
/n/10 , \T1/txtt/m/n/10 holdout_duration\T1/qtm/m/n/10 ) or (\T1/txtt/m/n/10 ho
ldout_start_date\T1/qtm/m/n/10 ,

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[]\T1/qtm/m/n/10 [str] one of \T1/txtt/m/n/10 datarobot.enums.

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\T1/txtt/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/qtm/m/n/10 . Whethe
r mod-els cre-ated by the

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

[513] [514]
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[]\T1/qtm/m/n/10 (New in ver-sion v2.24)
[515]
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[]\T1/qtm/m/n/10 Op-tion 2 (New in ver-sion v2.20): Use \T1/txtt/m/n/10 index\T
1/qtm/m/n/10 , \T1/txtt/m/n/10 primary_training_start_date\T1/qtm/m/n/10 ,
[516]
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[]\T1/qtm/m/n/10 [bool][Switch to us-ing the older a_priori key name in-stead o
f
[517] [518]
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[]\T1/qtm/m/n/10 [str] one of \T1/txtt/m/n/10 datarobot.enums.

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\T1/txtt/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/qtm/m/n/10 . Whethe
r mod-els cre-ated by the
[519] [520]
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[]\T1/qtm/m/n/10 (New in ver-sion v2.24)
[521] [522] [523]
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[]\T1/qtm/m/n/10 [bool, op-tional] De-faults to \T1/txtt/m/n/10 False\T1/qtm/m/
n/10 . If \T1/txtt/m/n/10 False\T1/qtm/m/n/10 ,

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\T1/qtm/m/n/10 will use a duration-based ap-proach for spec-i-fy-ing back-tests
 (\T1/txtt/m/n/10 gap_duration\T1/qtm/m/n/10 ,
[524] [525]
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[]\T1/qtm/m/n/10 This is the spec-i-fi-ca-tion that should be passed to [][]\T1
/txtt/m/sl/10 Project.analyze_and_model[][] \T1/qtm/m/n/10 via the

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\T1/txtt/m/n/10 partitioning_method \T1/qtm/m/n/10 pa-ram-e-ter. To see the ful
l par-ti-tion-ing use [][]\T1/txtt/m/sl/10 DatetimePartitioning.
[526] [527]
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[]\T1/qtm/m/n/10 [pandas.Dataframe] the back-test at-tributes, for-mat-ted into
 a
[528] [529] [530] [531] [532] [533] [534] [535] [536] [537]
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[]\T1/qtm/m/n/10 Com-pute a Pre-dic-tion-Ex-pla-na-tion-sIni-tial-iza-tion for 
the model via \T1/txtt/m/n/10 datarobot.

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[]\T1/qtm/m/n/10 Com-pute pre-dic-tions for the model and dataset via \T1/txtt/
m/n/10 datarobot.Model.
[538] [539] [540]
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\T1/qtm/m/it/10 tion_explanations=None\T1/qtm/m/n/10 , 

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

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\T1/qtm/m/it/10 justed_prediction_values=None\T1/txtt/m/n/10 )|  
[541]
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[]\T1/qtm/m/n/10 [list] an ar-ray of dic-tio-nar-ies with a schema de-scribed a
s

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

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\T1/qtm/m/it/10 ment_method=None\T1/txtt/m/n/10 )|  
[542] [543] [544] [545] [546] [547] [548] [549]
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[]\T1/qtm/m/n/10 Raised if an ob-ject passed to the \T1/txtt/m/n/10 options \T1
/qtm/m/n/10 pa-ram-e-ter is not an
[550] [551] [552] [553] [554] [555]
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[]\T1/qtm/m/n/10 [PartitioningMethod ob-ject, op-tional] In-stance of one of th
e

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[][]\T1/qtm/m/it/10 Par-ti-tion Classes[][] \T1/qtm/m/n/10 de-fined in \T1/txtt
/m/n/10 datarobot.helpers.partitioning_methods\T1/qtm/m/n/10 .

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\T1/qtm/m/n/10 As an al-ter-na-tive, use [][]\T1/txtt/m/sl/10 Project.set_parti
tioning_method[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 Project.

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[]\T1/qtm/m/n/10 Com-pute “All back-test”
[556]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[557]
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[]\T1/qtm/m/n/10 Project.list(search_params={‘project_name’: ‘red’}) >>
>
[558]
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[]\T1/qtm/m/n/10 [PartitioningMethod ob-ject, op-tional] In-stance of one of th
e

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[][]\T1/qtm/m/it/10 Par-ti-tion Classes[][] \T1/qtm/m/n/10 de-fined in \T1/txtt
/m/n/10 datarobot.helpers.partitioning_methods\T1/qtm/m/n/10 .

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\T1/qtm/m/n/10 As an al-ter-na-tive, use [][]\T1/txtt/m/sl/10 Project.set_parti
tioning_method[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 Project.
[559] [560]
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[]\T1/qtm/m/n/10 [PartitioningMethod ob-ject, op-tional] In-stance of one of th
e

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[][]\T1/qtm/m/it/10 Par-ti-tion Classes[][] \T1/qtm/m/n/10 de-fined in \T1/txtt
/m/n/10 datarobot.helpers.partitioning_methods\T1/qtm/m/n/10 .

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\T1/qtm/m/n/10 As an al-ter-na-tive, use [][]\T1/txtt/m/sl/10 Project.set_parti
tioning_method[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 Project.
[561] [562] [563] [564] [565]
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[]\T1/qtm/m/n/10 Raised if \T1/txtt/m/n/10 forecast_point \T1/qtm/m/n/10 or \T1
/txtt/m/n/10 predictions_start_date \T1/qtm/m/n/10 and
[566] [567] [568] [569] [570] [571] [572] [573] [574] [575]
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).
[576]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[577]
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[]\T1/qtm/m/n/10 [str] Cho-sen blend method, one from \T1/txtt/m/n/10 datarobot
.enums.
[578]
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[]\T1/qtm/m/n/10 [str] Cho-sen blend method, one from \T1/txtt/m/n/10 datarobot
.enums.

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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

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[]\T1/qtm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
[581] [582] [583]
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[]\T1/qtm/m/n/10 (New in ver-sion v2.23) one of

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\T1/txtt/m/n/10 datarobot.enums.DATETIME_AUTOPILOT_DATA_SAMPLING_METHOD\T1/qtm/
m/n/10 . Ap-pli-ca-ble

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[]\T1/qtm/m/n/10 Time Se-ries only.

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

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\T1/qtm/m/n/10 cal-cu-lat-ing fair-ness. Can be one of \T1/txtt/m/n/10 proporti
onalParity\T1/qtm/m/n/10 , \T1/txtt/m/n/10 equalParity\T1/qtm/m/n/10 ,

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\T1/txtt/m/n/10 predictionBalance\T1/qtm/m/n/10 , \T1/txtt/m/n/10 trueFavorable
AndUnfavorableRateParity \T1/qtm/m/n/10 or
[584]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[585]
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[]\T1/qtm/m/n/10 [PartitioningMethod, op-tional] An in-stance of \T1/txtt/m/n/1
0 datarobot.
[586] [587] [588]
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[]\T1/qtm/m/n/10 If value of \T1/txtt/m/n/10 variable_type \T1/qtm/m/n/10 is no
t from \T1/txtt/m/n/10 datarobot.enums.
[589] [590] [591]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[592] [593]
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[]\T1/qtm/m/n/10 This is an al-ter-na-tive to pass-ing a spec-i-fi-ca-tion to [
][]\T1/txtt/m/sl/10 Project.analyze_and_model[][] \T1/qtm/m/n/10 via the
[594] [595] [596] [597] [598] [599] [600] [601] [602] [603] [604] [605]
[606] [607]
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[]\T1/qtm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
[608] [609] [610] [611] [612] [613] [614] [615]
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[]

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[]

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[]
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[]\T1/qtm/m/n/10 A

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[]
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[]

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[]\T1/qtm/m/n/10 The list of
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[]\T1/qtm/m/n/10 De-

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[]\T1/qtm/m/n/10 The

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\T1/qtm/m/it/10 last_modifier_full_name=None\T1/qtm/m/n/10 , 
[628]
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[]
[629] [630] [631] [632] [633] [634] [635] [636] [637] [638]
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\T1/qtm/m/it/10 tive_feature_derivation_window_start=None\T1/qtm/m/n/10 , 

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[639]
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[]\T1/qtm/m/n/10 The back-test
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[]\T1/qtm/m/it/10 document_index\T1/qtm/m/n/10 , 

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[645] [646] [647] [648] [649] [650] [651] [652] [653] [654]
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[]\T1/qtm/m/n/10 Added [][]\T1/txtt/m/sl/10 DatetimePartitioningId[][]\T1/qtm/m
/n/10 , which can be passed as a \T1/qtm/m/it/10 par-ti-tion-ing_method \T1/qtm
/m/n/10 to [][]\T1/txtt/m/sl/10 Project.

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[]

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[][]\T1/txtt/m/sl/10 Deployment.get_bias_and_fairness_settings[][] [][]Deployme
nt.update_bias_and_fairness_settings[][] 

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\T1/qtm/m/n/10 and run_once [][]\T1/txtt/m/sl/10 BatchMonitoringJobDefinition.l
ist[][] [][]BatchMonitoringJobDefinition.

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\T1/txtt/m/sl/10 get[][] [][]BatchMonitoringJobDefinition.create[][] [][]BatchM
onitoringJobDefinition.update[][]

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[][]\T1/txtt/m/sl/10 BatchMonitoringJobDefinition.delete[][] [][]BatchMonitorin
gJobDefinition.run_on_schedule[][]

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\T1/txtt/m/sl/10 create_from_url[][] [][]Dataset.create_from_data_source[][] []
[]Dataset.create_from_query_generator[][]

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[][]\T1/txtt/m/sl/10 Dataset.create_project[][] [][]Project.create[][] [][]Proj
ect.create_from_data_source[][] [][]Project.

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\T1/txtt/m/sl/10 create_from_dataset[][] [][]Project.create_segmented_project_f
rom_clustering_model[][] [][]Project.

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[]\T1/qtm/m/n/10 Added method meth:\T1/qtm/m/it/10 Deployment.get_predictions_o
ver_time <datarobot.models.Deployment.get_predictions_over_time>
[655]
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\T1/txtt/m/sl/10 PredictionExplanations.get_all_as_dataframe()[][] \T1/qtm/m/n/
10 and [][]\T1/txtt/m/sl/10 datarobot.PredictionExplanations.

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[]\T1/qtm/m/n/10 Ex-tended [][]\T1/txtt/m/sl/10 CustomInferenceModel.update[][]
 \T1/qtm/m/n/10 and [][]\T1/txtt/m/sl/10 CustomInferenceModel.update[][] \T1/qt
m/m/n/10 with
[656]
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[]\T1/txtt/m/n/10 DatetimeModel.get_feature_fit_metadata \T1/qtm/m/n/10 has bee
n re-moved. Use [][]\T1/txtt/m/sl/10 DatetimeModel.

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[]\T1/txtt/m/n/10 DatetimeModel.request_feature_fit \T1/qtm/m/n/10 has been re-
moved. Use [][]\T1/txtt/m/sl/10 DatetimeModel.

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[]\T1/txtt/m/n/10 Model.get_or_request_feature_fit \T1/qtm/m/n/10 has been re-m
oved. Use [][]\T1/txtt/m/sl/10 Model.

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[]\T1/txtt/m/n/10 DatetimeModel.get_or_request_feature_fit \T1/qtm/m/n/10 has b
een re-moved. Use [][]\T1/txtt/m/sl/10 DatetimeModel.
[657]
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[]

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[]

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[]\T1/qtm/m/n/10 Added new meth-ods [][]\T1/txtt/m/sl/10 BatchPredictionJob.app
ly_time_series_data_prep_and_score[][] \T1/qtm/m/n/10 and

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[][]\T1/txtt/m/sl/10 BatchPredictionJob.apply_time_series_data_prep_and_score_t
o_file[][] \T1/qtm/m/n/10 that ap-ply time se-ries

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[]\T1/qtm/m/n/10 Added new meth-ods [][]\T1/txtt/m/sl/10 DataEngineQueryGenerat
or.prepare_prediction_dataset[][] \T1/qtm/m/n/10 and

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[][]\T1/txtt/m/sl/10 DataEngineQueryGenerator.prepare_prediction_dataset_from_c
atalog[][] \T1/qtm/m/n/10 that ap-ply time se-ries
[658]
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[]\T1/qtm/m/n/10 Added two new meth-ods to the Im-ageAug-men-ta-tion-List class
: [][]\T1/txtt/m/sl/10 ImageAugmentationList.list[][]\T1/qtm/m/n/10 ,
[659]
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[]\T1/qtm/m/n/10 Re-stored [][]\T1/txtt/m/sl/10 Model.get_leaderboard_ui_permal
ink[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 Model.open_model_browser[][]\T1/qt
m/m/n/10 , [][]\T1/txtt/m/sl/10 Project.
[660] [661]
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[]\T1/qtm/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/qtm/m/n/10 Added in-for-ma-tion about ver-tex to ad-vanced tun-ing pa-ram
-e-ters [][]\T1/txtt/m/sl/10 datarobot.models.Model.
[662]
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[][][]\T1/txtt/m/sl/10 CustomModelVersion[][]\T1/qtm/m/n/10 ’s \T1/txtt/m/n/1
0 required_metadata \T1/qtm/m/n/10 pa-ram-e-ter has been re-moved. Use

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[][][]\T1/txtt/m/sl/10 CustomTaskVersion[][]\T1/qtm/m/n/10 ’s \T1/txtt/m/n/10
 required_metadata \T1/qtm/m/n/10 pa-ram-e-ter has been re-moved. Use

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[]\T1/qtm/m/n/10 The [][]\T1/txtt/m/sl/10 Deployment[][] \T1/qtm/m/n/10 method 
\T1/txtt/m/n/10 create_from_custom_model_image \T1/qtm/m/n/10 was re-moved. Use
 [][]\T1/txtt/m/sl/10 Deployment.
[663]
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[]\T1/qtm/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-

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\T1/qtm/m/n/10 ing: - [][]\T1/txtt/m/sl/10 DocumentTextExtractionSample[][] \T1
/qtm/m/n/10 - [][]\T1/txtt/m/sl/10 DocumentTextExtractionSamplePage[][] \T1/qtm
/m/n/10 -

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[]\T1/qtm/m/n/10 Added sup-port to pass cal-cu-la-tion mode to pre-dic-tion ex-
pla-na-tions (\T1/qtm/m/it/10 mode \T1/qtm/m/n/10 pa-ram-e-ter in

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\T1/qtm/m/n/10 - [][]\T1/txtt/m/sl/10 Project.apply_bias_mitigation[][] \T1/qtm
/m/n/10 - [][]\T1/txtt/m/sl/10 Project.request_bias_mitigation_feature_info[][]


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\T1/qtm/m/n/10 - [][]\T1/txtt/m/sl/10 Project.get_bias_mitigation_feature_info[
][] \T1/qtm/m/n/10 and by adding new bias mit-

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\T1/qtm/m/n/10 i-ga-tion params - bias_mitigation_feature_name - bias_mitigatio
n_technique - in-

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\T1/qtm/m/n/10 clude_bias_mitigation_feature_as_predictor_variable to the ex-is
t-ing method - [][]\T1/txtt/m/sl/10 Project.start[][] \T1/qtm/m/n/10 and

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\T1/qtm/m/n/10 by adding this enum to sup-ply params to some of the above func-
tion-al-ity \T1/txtt/m/n/10 datarobot.enums.
[664] [665]
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[]\T1/qtm/m/n/10 Up-date the doc-u-men-ta-tion to sug-gest that \T1/qtm/m/it/10
 fea-ture_derivation_window_end \T1/qtm/m/n/10 of [][]\T1/txtt/m/sl/10 datarobo
t.

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[]\T1/qtm/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
[666] [667]
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[]

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[]\T1/txtt/m/n/10 DatetimeModel.get_feature_fit <datarobot.models.DatetimeModel
.

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[]\T1/qtm/m/n/10 Dep-re-cat-ing sca-le-out pa-ram-e-ters for projects / mod-els
. In-cludes \T1/txtt/m/n/10 scaleout_modeling_mode\T1/qtm/m/n/10 ,
[668]
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[]\T1/qtm/m/n/10 Added the abil-ity to up-load a pre-dic-tion dataset into a pr
oject from the AI cat-a-log [][]\T1/txtt/m/sl/10 Project.

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[]\T1/qtm/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/qtm/m/n/10 Added the abil-ity to com-pute and re-trieve Fea-ture Ef-fects
 for a Mul-ti-class model us-

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\T1/qtm/m/n/10 ing [][]\T1/txtt/m/sl/10 datarobot.models.Model.request_feature_
effects_multiclass()[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 datarobot.

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\T1/txtt/m/sl/10 models.Model.get_feature_effects_multiclass()[][] \T1/qtm/m/n/
10 or [][]\T1/txtt/m/sl/10 datarobot.models.Model.

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\T1/txtt/m/sl/10 get_or_request_feature_effects_multiclass()[][] \T1/qtm/m/n/10
 meth-ods. For date-time mod-els use fol-low-

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
\T1/qtm/m/n/10 ing meth-ods [][]\T1/txtt/m/sl/10 datarobot.models.DatetimeModel
.request_feature_effects_multiclass()[][]\T1/qtm/m/n/10 ,

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
[][]\T1/txtt/m/sl/10 datarobot.models.DatetimeModel.get_feature_effects_multicl
ass()[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 datarobot.models.
[669] [670]
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[]\T1/qtm/m/n/10 fix [][]\T1/txtt/m/sl/10 datarobot.CustomTaskVersion[][] \T1/q
tm/m/n/10 and [][]\T1/txtt/m/sl/10 datarobot.CustomModelVersion[][] \T1/qtm/m/n
/10 to cor-rectly for-mat

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[]\T1/qtm/m/n/10 Added the abil-ity to re-quest a spe-cific model be pre-pared 
for de-ploy-ment us-ing [][]\T1/txtt/m/sl/10 Project.
[671] [672] [673]
Underfull \hbox (badness 10000) in paragraph at lines 68611--68614
[]\T1/qtm/m/n/10 The \T1/txtt/m/n/10 required_metadata \T1/qtm/m/n/10 prop-erty
 of [][]\T1/txtt/m/sl/10 datarobot.CustomModelVersion[][] \T1/qtm/m/n/10 has be
en dep-re-cated.

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[]\T1/qtm/m/n/10 The \T1/txtt/m/n/10 required_metadata \T1/qtm/m/n/10 prop-erty
 of [][]\T1/txtt/m/sl/10 datarobot.CustomTaskVersion[][] \T1/qtm/m/n/10 has bee
n dep-re-cated.
[674]
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[]\T1/qtm/m/n/10 Sup-port for Batch Pre-dic-tion Job Def-i-ni-tions has now bee
n added through the fol-low-ing class:

Underfull \hbox (badness 10000) in paragraph at lines 68829--68832
[]\T1/qtm/m/n/10 Added method to down-load fea-ture dis-cov-ery recipe SQLs (li
m-ited beta fea-ture). [][]\T1/txtt/m/sl/10 Project.
[675]
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[]\T1/qtm/m/n/10 Par-tial his-tory pre-dic-tions can be made with time se-ries 
time se-ries mul-ti-series mod-els us-

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\T1/qtm/m/n/10 ing the \T1/txtt/m/n/10 allow_partial_history_time_series_predic
tions \T1/qtm/m/n/10 at-tribute of the [][]\T1/txtt/m/sl/10 datarobot.

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[][]\T1/txtt/m/sl/10 PairwiseJointProbabilities[][] \T1/qtm/m/n/10 and [][]\T1/
txtt/m/sl/10 PairwiseConditionalProbabilities[][] \T1/qtm/m/n/10 or [][]\T1/txt
t/m/sl/10 Feature.

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\T1/txtt/m/sl/10 get_pairwise_correlations[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/s
l/10 Feature.get_pairwise_joint_probabilities[][] \T1/qtm/m/n/10 and [][]\T1/tx
tt/m/sl/10 Feature.

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[]

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[676]
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[]\T1/qtm/m/n/10 Sup-port for down-load-ing fea-ture dis-cov-ery train-ing or p
re-dic-tion dataset us-ing [][]\T1/txtt/m/sl/10 Project.

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[]\T1/qtm/m/n/10 Added [][]\T1/txtt/m/sl/10 datarobot.models.FeatureAssociation
Matrix[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 datarobot.models.

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\T1/txtt/m/sl/10 FeatureAssociationMatrixDetails[][] \T1/qtm/m/n/10 and [][]\T1
/txtt/m/sl/10 datarobot.models.FeatureAssociationFeaturelists[][]

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[]\T1/qtm/m/n/10 Re-move \T1/qtm/m/it/10 de-sired_memory \T1/qtm/m/n/10 param f
rom the fol-low-ing classes: [][]\T1/txtt/m/sl/10 datarobot.CustomInferenceMode
l[][]\T1/qtm/m/n/10 ,

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[]\T1/qtm/m/n/10 Re-move \T1/txtt/m/n/10 desired_memory \T1/qtm/m/n/10 param fr
om the fol-low-ing meth-ods: [][]\T1/txtt/m/sl/10 CustomInferenceModel.create[]
[]\T1/qtm/m/n/10 ,

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[][]\T1/txtt/m/sl/10 CustomModelVersion.create_clean[][]\T1/qtm/m/n/10 , [][]\T
1/txtt/m/sl/10 CustomModelVersion.create_clean[][]\T1/qtm/m/n/10 , [][]\T1/txtt
/m/sl/10 CustomModelTest.
[677]
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[]

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[]

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[]\T1/qtm/m/n/10 Fea-ture dis-cov-ery projects can now be cre-ated us-ing the [
][]\T1/txtt/m/sl/10 Project.start[][] \T1/qtm/m/n/10 method by pro-vid-ing

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[678]
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[]\T1/qtm/m/n/10 New \T1/txtt/m/n/10 sampling_method \T1/qtm/m/n/10 param in []
[]\T1/txtt/m/sl/10 Model.train_datetime[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/1
0 Project.train_datetime[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 Model.

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[]

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[][][]\T1/txtt/m/sl/10 SecondaryDatasetConfigurations.

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[]\T1/qtm/m/n/10 Class \T1/txtt/m/n/10 FeatureEngineeringGraph \T1/qtm/m/n/10 h
as been re-moved. Use [][]\T1/txtt/m/sl/10 datarobot.models.
[679]
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[]\T1/qtm/m/n/10 for [][]\T1/txtt/m/sl/10 datarobot.

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[]\T1/qtm/m/n/10 for [][]\T1/txtt/m/sl/10 datarobot.models.

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[][][]\T1/txtt/m/sl/10 datarobot.

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[]\T1/qtm/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:
[680] [681]
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[]\T1/qtm/m/n/10 Added new ar-gu-ments \T1/txtt/m/n/10 explanation_algorithm \T
1/qtm/m/n/10 and \T1/txtt/m/n/10 max_explanations \T1/qtm/m/n/10 to method [][]
\T1/txtt/m/sl/10 Model.

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[]\T1/qtm/m/n/10 Added new ar-gu-ments \T1/txtt/m/n/10 explanation_algorithm \T
1/qtm/m/n/10 and \T1/txtt/m/n/10 max_explanations \T1/qtm/m/n/10 to method [][]
\T1/txtt/m/sl/10 Model.
[682] [683]
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[]\T1/qtm/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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[]\T1/qtm/m/n/10 These meth-ods now pro-vide ad-di-tional meta-data fields in F
ea-ture Im-pact re-sults if called with

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[]\T1/qtm/m/n/10 Sec-ondary dataset con-fig-u-ra-tion re-trieve and dele-tion i
s eas-ier now though new

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[][]\T1/txtt/m/sl/10 SecondaryDatasetConfigurations.delete[][] \T1/qtm/m/n/10 s
oft deletes a Sec-ondary dataset con-fig-u-ra-tion.
[684]
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[]

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[685]
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[]\T1/qtm/m/n/10 A new boolean at-tribute \T1/txtt/m/n/10 unsupervised_mode \T1
/qtm/m/n/10 was added to [][]\T1/txtt/m/sl/10 datarobot.
[686]
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[]\T1/qtm/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/txtt/m/sl/10 Deployment.get_prediction_intervals_settings[][] \T1/qtm/m
/n/10 and [][]\T1/txtt/m/sl/10 Deployment.
[687]
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[]\T1/qtm/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/txtt/m/sl/10 Project.

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[]\T1/qtm/m/n/10 Method [][]\T1/txtt/m/sl/10 Project.set_target[][] \T1/qtm/m/n
/10 sup-port new op-tional pa-ram-e-ters \T1/txtt/m/n/10 featureEngineeringGrap
hs \T1/qtm/m/n/10 and

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[]\T1/qtm/m/n/10 Se-ries ac-cu-racy re-trieval meth-ods ([][]\T1/txtt/m/sl/10 D
atetimeModel.get_series_accuracy_as_dataframe[][] \T1/qtm/m/n/10 and
[688]
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[]\T1/qtm/m/n/10 Mono-tonic con-straints are now sup-ported for OTV projects. T
o that end, the pa-ram-e-ters

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\T1/txtt/m/n/10 monotonic_increasing_featurelist_id \T1/qtm/m/n/10 and \T1/txtt
/m/n/10 monotonic_decreasing_featurelist_id \T1/qtm/m/n/10 can be
[689] [690] [691]
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[]\T1/qtm/m/n/10 Pre-vi-ously avail-able for only Eu-reqa mod-els, Ad-vanced Tu
n-ing meth-ods and ob-jects, in-clud-ing

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[][]\T1/txtt/m/sl/10 Model.start_advanced_tuning_session[][]\T1/qtm/m/n/10 , []
[]\T1/txtt/m/sl/10 Model.get_advanced_tuning_parameters[][]\T1/qtm/m/n/10 , [][
]\T1/txtt/m/sl/10 Model.

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[][][]\T1/txtt/m/sl/10 Project.upload_dataset[][] \T1/qtm/m/n/10 and [][]\T1/tx
tt/m/sl/10 Project.upload_dataset_from_data_source[][] \T1/qtm/m/n/10 will re-t
urn a
[692] [693]
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[]\T1/qtm/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/qtm/m/n/10 Cross se-ries fea-tures can now be cre-ated within time se-rie
s mul-ti-series projects us-ing

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\T1/qtm/m/n/10 the \T1/txtt/m/n/10 use_cross_series_features \T1/qtm/m/n/10 and
 \T1/txtt/m/n/10 aggregation_type \T1/qtm/m/n/10 at-tributes of the [][]\T1/txt
t/m/sl/10 datarobot.

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[]\T1/qtm/m/n/10 New \T1/txtt/m/n/10 number_of_known_in_advance_features \T1/qt
m/m/n/10 at-tribute has been added to the [][]\T1/txtt/m/sl/10 datarobot.
[694]
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[]\T1/qtm/m/n/10 Train-ing pre-dic-tions for date-time par-ti-tioned projects n
ow sup-port the new data sub-set

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[]\T1/qtm/m/n/10 New meth-ods and ob-jects, in-clud-ing [][]\T1/txtt/m/sl/10 Mo
del.start_advanced_tuning_session[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 Mode
l.

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\T1/txtt/m/sl/10 get_advanced_tuning_parameters[][]\T1/qtm/m/n/10 , [][]\T1/txt
t/m/sl/10 Model.advanced_tune[][]\T1/qtm/m/n/10 , and [][]\T1/txtt/m/sl/10 Adva
ncedTuningSession[][]\T1/qtm/m/n/10 , were

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[]\T1/qtm/m/n/10 A cus-tom pre-dic-tion thresh-old may now be con-fig-ured for 
each model via [][]\T1/txtt/m/sl/10 Model.

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[]\T1/qtm/m/n/10 In-di-vid-ual cross val-i-da-tion scores can be re-trieved for
 new mod-els us-ing [][]\T1/txtt/m/sl/10 Model.
[695]
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[][][]\T1/txtt/m/sl/10 ModelRecommendation.get[][] \T1/qtm/m/n/10 now ac-cepts 
an op-tional pa-ram-e-ter of type \T1/txtt/m/n/10 datarobot.enums.

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[]\T1/qtm/m/n/10 A new rec-om-men-da-tion type “Rec-om-mended for De-ploy-men
-t” has been added to
[696]
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[]\T1/qtm/m/n/10 Added a new fea-ture to re-trieve fea-ture logs for time se-ri
es projects. Check [][]\T1/txtt/m/sl/10 datarobot.

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\T1/txtt/m/sl/10 DatetimePartitioning.feature_log_list()[][] \T1/qtm/m/n/10 and
 [][]\T1/txtt/m/sl/10 datarobot.DatetimePartitioning.
[697] [698] [699] [700] [701] [702] [703] [704] [705]
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[]\T1/qtm/m/n/10 its
[706] [707] [708] [709]
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[]\T1/qtm/m/n/10 In sev-eral places where \T1/txtt/m/n/10 AppPlatformError \T1/
qtm/m/n/10 was be-ing raised, now \T1/txtt/m/n/10 TypeError\T1/qtm/m/n/10 , \T1
/txtt/m/n/10 ValueError \T1/qtm/m/n/10 or

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[]\T1/qtm/m/n/10 If a \T1/txtt/m/n/10 ClientError \T1/qtm/m/n/10 or \T1/txtt/m/
n/10 ServerError \T1/qtm/m/n/10 oc-curs dur-ing a call to \T1/txtt/m/n/10 Proje
ct.from_async\T1/qtm/m/n/10 , then a
[710]
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[]\T1/txtt/m/n/10 recommendation_settings \T1/qtm/m/n/10 pa-ram-e-ter of \T1/tx
tt/m/n/10 Project.start \T1/qtm/m/n/10 is dep-re-cated in fa-vor of
[711] [712] [713] [714] [715] (./datarobot-public-api-client.ind [716]
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[]\T1/txtt/m/n/10 AccuracyOverTime \T1/qtm/m/n/10 (\T1/qtm/m/it/10 class in

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[]\T1/txtt/m/n/10 AccuracyOverTimePlot \T1/qtm/m/n/10 (\T1/qtm/m/it/10 class in


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lass in

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[]\T1/txtt/m/n/10 AccuracyOverTimePlotsMetadata \T1/qtm/m/n/10 (\T1/qtm/m/it/10
 class in

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[]\T1/txtt/m/n/10 activate() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.D
eployment method\T1/qtm/m/n/10 ),

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[]\T1/txtt/m/n/10 add_to_project() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.mo
dels.user_blueprints.models.UserBlueprintAddToProjectMenu

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[]\T1/txtt/m/n/10 add_to_project() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.Us
erBlueprint class

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els.BlenderModel

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els.DatetimeModel

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[]\T1/txtt/m/n/10 advanced_tune() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.mod
els.Model method\T1/qtm/m/n/10 ),

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els.PrimeModel

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s in

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[]\T1/txtt/m/n/10 AdvancedTuningSession \T1/qtm/m/n/10 (\T1/qtm/m/it/10 class i
n

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.models.Project

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ass in

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 class in

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 in

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10 class in

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ass in

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[]\T1/txtt/m/n/10 AnomalyOverTimePlotsMetadata \T1/qtm/m/n/10 (\T1/qtm/m/it/10 
class in

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[]\T1/txtt/m/n/10 apply_time_series_data_prep_and_score()

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[]\T1/txtt/m/n/10 as_dataframe() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.mode
ls.PairwiseConditionalProbabilities

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ls.PairwiseCorrelations

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ls.PairwiseJointProbabilities

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[]\T1/txtt/m/n/10 assign_training_data()

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egmentationTask

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ortedModel class

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taEngineQueryGenerator

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api_object.APIObject

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
BlenderModel class

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
Blueprint class

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
DatetimeModel class

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
Deployment class

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
Featurelist class

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
PayoffMatrix class

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
PrimeModel class

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
RatingTableModel

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
segmentation.SegmentationTask

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.Segment
ationTask class

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[]\T1/txtt/m/n/10 from_job() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.P
redictJob class
[723]
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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
CustomTask class

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
CustomTaskVersion

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
DataSource class

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
DataStore class

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models.api_object.APIObject

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
models.Blueprint

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
models.Deployment

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
models.Featurelist

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
models.pareto_front.ParetoFront

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[]\T1/txtt/m/n/10 from_server_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.
models.PayoffMatrix

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[]\T1/txtt/m/n/10 generate() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.Datetime
Partitioning class

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[]\T1/txtt/m/n/10 generate() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.a
utomated_documentation.AutomatedDocument

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[]\T1/txtt/m/n/10 generate_optimized()

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.models.model_lineage.ModelLineage

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.models.notebooks.Notebook

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.models.retraining.RetrainingPolicy

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ryGenerator class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.ExecutionEnvi
ronmentVersion class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.anomal
y_assessment.AnomalyAssessmentExplanations

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.anomal
y_assessment.AnomalyAssessmentPredictionsPreview

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BatchM
onitoringJob class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BatchP
redictionJob class

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redictionJobDefinition

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ance_doc_template.ComplianceDocTemplate

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ModelVersionConversion

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ment.Accuracy class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.deploy
ment.bias_and_fairness.FairnessScoresOverTime

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ment.PredictionsOverTime
[724]
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ment.ServiceStatsOverTime

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ment.TargetDrift class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.extern
al_baseline_validation.ExternalBaselineValidationInfo

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Featur
eAssociationMatrixDetails

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Featur
eHistogram class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Featur
eImpactJob class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Intera
ctionFeature class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.missin
g_report.MissingValuesReport

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.ModelB
lueprintChart class

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ngFeature class

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ngFeaturelist class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Multic
ategoricalHistogram

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Pairwi
seConditionalProbabilities

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Pairwi
seCorrelations class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Pairwi
seJointProbabilities

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.predic
tion_explanations.PredictionExplanationsPage

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Predic
tionDataset class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Rating
TableModel class

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Relati
onshipsConfiguration

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Second
aryDatasetConfigurations

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.segmen
tation.SegmentationTask

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.traini
ng_predictions.TrainingPredictions

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.visual
ai.ImageAugmentationOptions

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[]\T1/txtt/m/n/10 get() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.PredictionExp
lanationsInitialization

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[]\T1/txtt/m/n/10 get_access_list() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.C
alendarFile class

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[]\T1/txtt/m/n/10 get_access_list() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.D
ataStore method\T1/qtm/m/n/10 ),

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[]\T1/txtt/m/n/10 get_access_list() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.m
odels.Project

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[]\T1/txtt/m/n/10 get_accuracy() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.mode
ls.Deployment

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[]\T1/txtt/m/n/10 get_accuracy_over_time()

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Deployment method\T1/qtm/m/n/1
0 ),

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[]\T1/txtt/m/n/10 get_accuracy_over_time_plot()

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.DatetimeModel method\T1/qtm/m/
n/10 ),

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[]\T1/txtt/m/n/10 get_accuracy_over_time_plot_preview()

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.DatetimeModel method\T1/qtm/m/
n/10 ),
[725]
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[]\T1/txtt/m/n/10 get_accuracy_over_time_plots_metadata()

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.DatetimeModel method\T1/qtm/m/
n/10 ),

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[]\T1/txtt/m/n/10 get_active_combined_model()

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[]\T1/txtt/m/n/10 get_advanced_tuning_parameters()

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BlenderModel method\T1/qtm/m/n
/10 ),

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[]\T1/txtt/m/n/10 get_advanced_tuning_parameters()

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n/10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.PrimeModel method\T1/qtm/m/n/1
0 ),

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[]\T1/txtt/m/n/10 get_all() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Mo
delRecommendation

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10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.training_predictions.TrainingP
redictions

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m/n/10 ),

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/10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.DatetimeModel method\T1/qtm/m/
n/10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.PrimeModel method\T1/qtm/m/n/1
0 ),

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models.Dataset

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[]\T1/txtt/m/n/10 get_all_jobs() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.mode
ls.Project method\T1/qtm/m/n/10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BlenderModel method\T1/qtm/m/n
/10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.DatetimeModel method\T1/qtm/m/
n/10 ),

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[]\T1/txtt/m/n/10 get_all_lift_charts() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarob
ot.models.Model

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.PrimeModel method\T1/qtm/m/n/1
0 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BlenderModel method\T1/qtm/m/n
/10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.DatetimeModel method\T1/qtm/m/
n/10 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.PrimeModel method\T1/qtm/m/n/1
0 ),

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BlenderModel method\T1/qtm/m/n
/10 ),

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[1] [2] [1] [2]
Chapter 1.
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Chapter 2.
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[][][]\T1/txtt/m/sl/10 Dataset.create_from_url[][] \T1/qtm/m/n/10 takes .csv da
ta from a URL. If you have set

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[][][]\T1/txtt/m/sl/10 Dataset.create_from_data_source[][] \T1/qtm/m/n/10 takes
 data from a data source. If you have set
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[]\T1/qtm/m/n/10 For mul-ti-class mod-els, you can get a list of per-class lift
 charts us-ing the \T1/txtt/m/n/10 Model \T1/qtm/m/n/10 method

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[]\T1/qtm/m/n/10 Just as with the lift and ROC charts, you can use \T1/txtt/m/n
/10 Model \T1/qtm/m/n/10 meth-ods \T1/txtt/m/n/10 get_residuals_chart \T1/qtm/m
/n/10 and
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[]\T1/qtm/m/n/10 gap_duration (str): A du-ra-tion string such as those re-turne
d by the [][]\T1/txtt/m/sl/10 partitioning_methods.
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\T1/qtm/m/n/10 and \T1/txtt/m/n/10 validation_start_date\T1/qtm/m/n/10 . Us-ing
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0 and

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[]\T1/qtm/m/n/10 For date-time par-ti-tioned model you can re-trieve the Ac-cu-
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[][]\T1/txtt/m/sl/10 DatetimeModel.get_accuracy_over_time_plot[][]\T1/qtm/m/n/1
0 . You can also re-trieve the de-tailed meta-data us-ing
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[]\T1/qtm/m/n/10 Pre-dic-tion in-ter-vals can be cal-cu-lated for a [][]\T1/txt
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timeModel.
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[]\T1/qtm/m/n/10 1. Up-load a pre-dic-tion dataset, spec-i-fy-ing \T1/txtt/m/n/
10 predictions_start_date\T1/qtm/m/n/10 , \T1/txtt/m/n/10 predictions_end_date\
T1/qtm/m/n/10 , and
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[]\T1/qtm/m/n/10 Up-load 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/qtm/b/n/10 Note:  \T1/txtt/m/n/10 PredictionExplanations.get_all_as_datafra
me() \T1/qtm/m/n/10 and \T1/txtt/m/n/10 PredictionExplanations.
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[]\T1/qtm/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/txtt/m/n/10 PredictJob.
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[]\T1/qtm/m/n/10 In or-der to sched-ule the ex-e-cu-tion of a Batch Pre-dic-tio
n job, a def-i-ni-tion must first be cre-ated, us-

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\T1/qtm/m/n/10 of [][]\T1/txtt/m/sl/10 datarobot.models.deployment.DeploymentGr
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[]\T1/qtm/m/n/10 Re-fer to [][]\T1/txtt/m/sl/10 ExecutionEnvironment[][] \T1/qt
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[]\T1/qtm/m/n/10 cus-tom_model.update(is_training_data_for_versions_permanently
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[]\T1/qtm/m/n/10 Use Cases sup-port \T1/txtt/m/n/10 SHARING_ROLE.OWNER\T1/qtm/m
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RING_ROLE.CONSUMER \T1/qtm/m/n/10 and
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[]\T1/qtm/m/n/10 [str] the full JDBC url, for ex-am-ple \T1/qtm/m/it/10 jdbc:po
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[]\T1/qtm/m/n/10 A list of \T1/txtt/m/n/10 datarobot.enums.DATETIME_TREND_PLOTS
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[]\T1/qtm/m/n/10 A list of \T1/txtt/m/n/10 datarobot.enums.DATETIME_TREND_PLOTS
_RESOLUTION\T1/qtm/m/n/10 ,
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[]\T1/qtm/m/n/10 A list of \T1/txtt/m/n/10 datarobot.enums.DATETIME_TREND_PLOTS
_RESOLUTION\T1/qtm/m/n/10 ,
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[]\T1/qtm/m/n/10 datarobot-key: str. Cor-re-sponds the to the \T1/txtt/m/n/10 P
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[]\T1/qtm/m/n/10 {‘key’:’DataRobot’, ‘-sum-ma-ry’:{‘min’:0, ‘
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\T1/qtm/m/n/10 max’:29815.0, ‘st-d-De-v’:6498.029, ‘mean’:1490.75, �
�-me-di-
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[]\T1/qtm/m/n/10 His-togram in-for-ma-tion as a mul-ti-cat-e-gor-i-cal_histogra
m. The
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[]\T1/qtm/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/qtm/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/txtt/m/n/10 pairwise_joint_probabilities.statistic_dataframes[(0,1)].loc[\T
S1/txtt/m/n/10 '\T1/txtt/m/n/10 A\TS1/txtt/m/n/10 '\T1/txtt/m/n/10 ,

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[]\T1/qtm/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/qtm/m/n/10 [list(dict)] List of con-di-tional prob-a-bil-ity val-ues with
 a schema de-scribed as

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[]\T1/qtm/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/txtt/m/n/10 pairwise_conditional_probabilities.statistic_dataframes[(0,1)].

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

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

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\T1/qtm/m/n/10 get pair-wise data for. Since ‘v2.19’ this is op-tional and 
de-faults to
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[]\T1/qtm/m/n/10 [enums.FEATURE_ASSOCIATION_TYPE] The type of de-

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\T1/qtm/m/n/10 pen-dence for the data. Since ‘v2.19’ this is op-tional and 
de-faults to
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[]\T1/qtm/m/n/10 List of fea-ture deriva-tion win-

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

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

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[]\T1/qtm/m/n/10 Clos-est value of pre-dic-
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[]\T1/qtm/m/n/10 Each el-e-ment is a \T1/txtt/m/n/10 datarobot.helpers.

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

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\T1/qtm/m/n/10 el-e-ment is a dic-tio-nary or a \T1/txtt/m/n/10 datarobot.helpe
rs.feature_discovery.
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[]\T1/qtm/m/n/10 The re-quested re-la-tion-ships

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

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

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\T1/qtm/m/n/10 el-e-ment is a dic-tio-nary or a \T1/txtt/m/n/10 datarobot.helpe
rs.feature_discovery.
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[]\T1/qtm/m/n/10 Col-umn(s) from the sec-ond
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[]\T1/qtm/m/n/10 List of fea-ture deriva-tion win-

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

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

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[]\T1/qtm/m/n/10 Clos-est value of pre-dic-
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[]\T1/qtm/m/n/10 cre-den-tials used by the sec-ondary

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[]\T1/qtm/m/n/10 list of sec-
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[]\T1/qtm/m/n/10 [list of Sec-ondary-Dataset-Con-fig-u-ra-tions] The re-
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[]\T1/qtm/m/it/10 ff_metadata_datetime_per_backtest\T1/txtt/m/n/10 )|  
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[]
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).
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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[437]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).
[443]
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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[444] [445]
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[]\T1/qtm/m/n/10 [str] which of train-ing_row_count, train-ing_duration, or tra
in-
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[]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[450] [451]
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[]\T1/qtm/m/n/10 The back-test

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[]\T1/qtm/m/n/10 The back-test

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[]\T1/qtm/m/n/10 The back-test
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[]\T1/qtm/m/n/10 [ForecastVsActualPlotPreview] a [][]\T1/txtt/m/sl/10 ForecastV
sActualPlotPreview[][] \T1/qtm/m/n/10 rep-re-sent-ing
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[]\T1/qtm/m/n/10 [AnomalyOverTimePlotPreview] a [][]\T1/txtt/m/sl/10 AnomalyOve
rTimePlotPreview[][] \T1/qtm/m/n/10 rep-re-sent-ing
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[473]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[474]
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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
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\T1/qtm/m/n/10 keys: \T1/txtt/m/n/10 featureName\T1/qtm/m/n/10 , \T1/txtt/m/n/1
0 impactNormalized\T1/qtm/m/n/10 , and \T1/txtt/m/n/10 impactUnnormalized\T1/qt
m/m/n/10 , and

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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[497] [498]
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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).

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[]\T1/qtm/m/n/10 All du-ra-tions should be spec-i-fied with a du-ra-tion string
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[][]\T1/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[504] [505] [506]
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[]\T1/qtm/m/n/10 [dict] “-Pa-ram-e-ter-s” dic-tio-nary, same as spec-i-fied
 on

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\T1/qtm/m/it/10 low_partial_history_time_series_predictions=False\T1/qtm/m/n/10
 , 

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[]\T1/qtm/m/n/10 This is the spec-i-fi-ca-tion that should be passed to [][]\T1
/txtt/m/sl/10 Project.analyze_and_model[][] \T1/qtm/m/n/10 via the

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[]\T1/qtm/m/n/10 Note that ei-ther (\T1/txtt/m/n/10 holdout_start_date\T1/qtm/m
/n/10 , \T1/txtt/m/n/10 holdout_duration\T1/qtm/m/n/10 ) or (\T1/txtt/m/n/10 ho
ldout_start_date\T1/qtm/m/n/10 ,

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[]\T1/qtm/m/n/10 [str] one of \T1/txtt/m/n/10 datarobot.enums.

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\T1/txtt/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/qtm/m/n/10 . Whethe
r mod-els cre-ated by the

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

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[]\T1/qtm/m/n/10 (New in ver-sion v2.24)
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[]\T1/qtm/m/n/10 Op-tion 2 (New in ver-sion v2.20): Use \T1/txtt/m/n/10 index\T
1/qtm/m/n/10 , \T1/txtt/m/n/10 primary_training_start_date\T1/qtm/m/n/10 ,
[516]
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[]\T1/qtm/m/n/10 [bool][Switch to us-ing the older a_priori key name in-stead o
f
[517] [518]
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[]\T1/qtm/m/n/10 [str] one of \T1/txtt/m/n/10 datarobot.enums.

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\T1/txtt/m/n/10 DATETIME_AUTOPILOT_DATA_SELECTION_METHOD\T1/qtm/m/n/10 . Whethe
r mod-els cre-ated by the
[519] [520]
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[]\T1/qtm/m/n/10 (New in ver-sion v2.24)
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[]\T1/qtm/m/n/10 [bool, op-tional] De-faults to \T1/txtt/m/n/10 False\T1/qtm/m/
n/10 . If \T1/txtt/m/n/10 False\T1/qtm/m/n/10 ,

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\T1/qtm/m/n/10 will use a duration-based ap-proach for spec-i-fy-ing back-tests
 (\T1/txtt/m/n/10 gap_duration\T1/qtm/m/n/10 ,
[524] [525]
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[]\T1/qtm/m/n/10 This is the spec-i-fi-ca-tion that should be passed to [][]\T1
/txtt/m/sl/10 Project.analyze_and_model[][] \T1/qtm/m/n/10 via the

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\T1/txtt/m/n/10 partitioning_method \T1/qtm/m/n/10 pa-ram-e-ter. To see the ful
l par-ti-tion-ing use [][]\T1/txtt/m/sl/10 DatetimePartitioning.
[526] [527]
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[]\T1/qtm/m/n/10 [pandas.Dataframe] the back-test at-tributes, for-mat-ted into
 a
[528] [529] [530] [531] [532] [533] [534] [535] [536] [537]
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[]\T1/qtm/m/n/10 Com-pute a Pre-dic-tion-Ex-pla-na-tion-sIni-tial-iza-tion for 
the model via \T1/txtt/m/n/10 datarobot.

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[]\T1/qtm/m/n/10 Com-pute pre-dic-tions for the model and dataset via \T1/txtt/
m/n/10 datarobot.Model.
[538] [539] [540]
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\T1/qtm/m/it/10 tion_explanations=None\T1/qtm/m/n/10 , 

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

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\T1/qtm/m/it/10 justed_prediction_values=None\T1/txtt/m/n/10 )|  
[541]
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[]\T1/qtm/m/n/10 [list] an ar-ray of dic-tio-nar-ies with a schema de-scribed a
s

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

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\T1/qtm/m/it/10 ment_method=None\T1/txtt/m/n/10 )|  
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[]\T1/qtm/m/n/10 Raised if an ob-ject passed to the \T1/txtt/m/n/10 options \T1
/qtm/m/n/10 pa-ram-e-ter is not an
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[]\T1/qtm/m/n/10 [PartitioningMethod ob-ject, op-tional] In-stance of one of th
e

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[][]\T1/qtm/m/it/10 Par-ti-tion Classes[][] \T1/qtm/m/n/10 de-fined in \T1/txtt
/m/n/10 datarobot.helpers.partitioning_methods\T1/qtm/m/n/10 .

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\T1/qtm/m/n/10 As an al-ter-na-tive, use [][]\T1/txtt/m/sl/10 Project.set_parti
tioning_method[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 Project.

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[]\T1/qtm/m/n/10 Com-pute “All back-test”
[556]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[557]
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[]\T1/qtm/m/n/10 Project.list(search_params={‘project_name’: ‘red’}) >>
>
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[]\T1/qtm/m/n/10 [PartitioningMethod ob-ject, op-tional] In-stance of one of th
e

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[][]\T1/qtm/m/it/10 Par-ti-tion Classes[][] \T1/qtm/m/n/10 de-fined in \T1/txtt
/m/n/10 datarobot.helpers.partitioning_methods\T1/qtm/m/n/10 .

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\T1/qtm/m/n/10 As an al-ter-na-tive, use [][]\T1/txtt/m/sl/10 Project.set_parti
tioning_method[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 Project.
[559] [560]
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[]\T1/qtm/m/n/10 [PartitioningMethod ob-ject, op-tional] In-stance of one of th
e

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[][]\T1/qtm/m/it/10 Par-ti-tion Classes[][] \T1/qtm/m/n/10 de-fined in \T1/txtt
/m/n/10 datarobot.helpers.partitioning_methods\T1/qtm/m/n/10 .

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\T1/qtm/m/n/10 As an al-ter-na-tive, use [][]\T1/txtt/m/sl/10 Project.set_parti
tioning_method[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 Project.
[561] [562] [563] [564] [565]
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[]\T1/qtm/m/n/10 Raised if \T1/txtt/m/n/10 forecast_point \T1/qtm/m/n/10 or \T1
/txtt/m/n/10 predictions_start_date \T1/qtm/m/n/10 and
[566] [567] [568] [569] [570] [571] [572] [573] [574] [575]
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[]\T1/qtm/m/n/10 [str, op-tional] Ei-ther \T1/txtt/m/n/10 validation \T1/qtm/m/
n/10 or \T1/txtt/m/n/10 crossValidation \T1/qtm/m/n/10 (also

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\T1/txtt/m/n/10 dr.SCORING_TYPE.validation \T1/qtm/m/n/10 or \T1/txtt/m/n/10 dr
.SCORING_TYPE.cross_validation\T1/qtm/m/n/10 ).
[576]
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[]\T1/qtm/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/txtt/m/sl/10 partitioning_methods.construct_duration_string[][] \T1/qtm
/m/n/10 helper method. Please see [][]\T1/qtm/m/it/10 date-time
[577]
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[]\T1/qtm/m/n/10 [str] Cho-sen blend method, one from \T1/txtt/m/n/10 datarobot
.enums.
[578]
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[]\T1/qtm/m/n/10 [str] Cho-sen blend method, one from \T1/txtt/m/n/10 datarobot
.enums.

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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with

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[]\T1/qtm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
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[]\T1/qtm/m/n/10 (New in ver-sion v2.23) one of

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\T1/txtt/m/n/10 datarobot.enums.DATETIME_AUTOPILOT_DATA_SAMPLING_METHOD\T1/qtm/
m/n/10 . Ap-pli-ca-ble

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[]\T1/qtm/m/n/10 Time Se-ries only.

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

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\T1/qtm/m/n/10 cal-cu-lat-ing fair-ness. Can be one of \T1/txtt/m/n/10 proporti
onalParity\T1/qtm/m/n/10 , \T1/txtt/m/n/10 equalParity\T1/qtm/m/n/10 ,

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\T1/txtt/m/n/10 predictionBalance\T1/qtm/m/n/10 , \T1/txtt/m/n/10 trueFavorable
AndUnfavorableRateParity \T1/qtm/m/n/10 or
[584]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[585]
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[]\T1/qtm/m/n/10 [PartitioningMethod, op-tional] An in-stance of \T1/txtt/m/n/1
0 datarobot.
[586] [587] [588]
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[]\T1/qtm/m/n/10 If value of \T1/txtt/m/n/10 variable_type \T1/qtm/m/n/10 is no
t from \T1/txtt/m/n/10 datarobot.enums.
[589] [590] [591]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[592] [593]
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[]\T1/qtm/m/n/10 This is an al-ter-na-tive to pass-ing a spec-i-fi-ca-tion to [
][]\T1/txtt/m/sl/10 Project.analyze_and_model[][] \T1/qtm/m/n/10 via the
[594] [595] [596] [597] [598] [599] [600] [601] [602] [603] [604] [605]
[606] [607]
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[]\T1/qtm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
[608] [609] [610] [611] [612] [613] [614] [615]
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[]

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[]

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[]
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[]\T1/qtm/m/n/10 A

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[]
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[]

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[]
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\T1/qtm/m/it/10 not_added_to_menu=None\T1/qtm/m/n/10 , 

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[]\T1/qtm/m/n/10 The list of
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\T1/qtm/m/it/10 **kwargs\T1/txtt/m/n/10 )|  

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[]
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[]\T1/qtm/m/n/10 A

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

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\T1/qtm/m/it/10 name\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 role\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 share_recipient_type\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 **kwargs\T1/txtt/m/n/10 )|  

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[]\T1/qtm/m/n/10 De-

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[]\T1/qtm/m/n/10 The

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\T1/qtm/m/it/10 info_creator_full_name\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 last_modifier_full_name=None\T1/qtm/m/n/10 , 
[628]
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[]
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\T1/qtm/m/it/10 tive_feature_derivation_window_start=None\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 tive_feature_derivation_window_end=None\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 is_n_clusters_dynamically_determined=None\T1/qtm/m/n/10 , 
[639]
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[]\T1/qtm/m/n/10 The back-test
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[]\T1/qtm/m/it/10 document_index\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 fea- 

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[]\T1/qtm/m/it/10 page_index\T1/qtm/m/n/10 , 

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[]\T1/qtm/m/n/10 Added [][]\T1/txtt/m/sl/10 DatetimePartitioningId[][]\T1/qtm/m
/n/10 , which can be passed as a \T1/qtm/m/it/10 par-ti-tion-ing_method \T1/qtm
/m/n/10 to [][]\T1/txtt/m/sl/10 Project.

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[]

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[][]\T1/txtt/m/sl/10 Deployment.get_bias_and_fairness_settings[][] [][]Deployme
nt.update_bias_and_fairness_settings[][] 

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\T1/qtm/m/n/10 and run_once [][]\T1/txtt/m/sl/10 BatchMonitoringJobDefinition.l
ist[][] [][]BatchMonitoringJobDefinition.

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\T1/txtt/m/sl/10 get[][] [][]BatchMonitoringJobDefinition.create[][] [][]BatchM
onitoringJobDefinition.update[][]

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[][]\T1/txtt/m/sl/10 BatchMonitoringJobDefinition.delete[][] [][]BatchMonitorin
gJobDefinition.run_on_schedule[][]

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\T1/txtt/m/sl/10 create_from_url[][] [][]Dataset.create_from_data_source[][] []
[]Dataset.create_from_query_generator[][]

Underfull \hbox (badness 10000) in paragraph at lines 66602--66615
[][]\T1/txtt/m/sl/10 Dataset.create_project[][] [][]Project.create[][] [][]Proj
ect.create_from_data_source[][] [][]Project.

Underfull \hbox (badness 10000) in paragraph at lines 66602--66615
\T1/txtt/m/sl/10 create_from_dataset[][] [][]Project.create_segmented_project_f
rom_clustering_model[][] [][]Project.

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[]\T1/qtm/m/n/10 Added method meth:\T1/qtm/m/it/10 Deployment.get_predictions_o
ver_time <datarobot.models.Deployment.get_predictions_over_time>
[655]
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\T1/txtt/m/sl/10 PredictionExplanations.get_all_as_dataframe()[][] \T1/qtm/m/n/
10 and [][]\T1/txtt/m/sl/10 datarobot.PredictionExplanations.

Underfull \hbox (badness 10000) in paragraph at lines 66746--66749
[]\T1/qtm/m/n/10 Ex-tended [][]\T1/txtt/m/sl/10 CustomInferenceModel.update[][]
 \T1/qtm/m/n/10 and [][]\T1/txtt/m/sl/10 CustomInferenceModel.update[][] \T1/qt
m/m/n/10 with
[656]
Underfull \hbox (badness 10000) in paragraph at lines 66787--66790
[]\T1/txtt/m/n/10 DatetimeModel.get_feature_fit_metadata \T1/qtm/m/n/10 has bee
n re-moved. Use [][]\T1/txtt/m/sl/10 DatetimeModel.

Underfull \hbox (badness 10000) in paragraph at lines 66797--66800
[]\T1/txtt/m/n/10 DatetimeModel.request_feature_fit \T1/qtm/m/n/10 has been re-
moved. Use [][]\T1/txtt/m/sl/10 DatetimeModel.

Underfull \hbox (badness 10000) in paragraph at lines 66812--66815
[]\T1/txtt/m/n/10 Model.get_or_request_feature_fit \T1/qtm/m/n/10 has been re-m
oved. Use [][]\T1/txtt/m/sl/10 Model.

Underfull \hbox (badness 10000) in paragraph at lines 66817--66820
[]\T1/txtt/m/n/10 DatetimeModel.get_or_request_feature_fit \T1/qtm/m/n/10 has b
een re-moved. Use [][]\T1/txtt/m/sl/10 DatetimeModel.
[657]
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[]

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[]

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[]\T1/qtm/m/n/10 Added new meth-ods [][]\T1/txtt/m/sl/10 BatchPredictionJob.app
ly_time_series_data_prep_and_score[][] \T1/qtm/m/n/10 and

Underfull \hbox (badness 7221) in paragraph at lines 66938--66942
[][]\T1/txtt/m/sl/10 BatchPredictionJob.apply_time_series_data_prep_and_score_t
o_file[][] \T1/qtm/m/n/10 that ap-ply time se-ries

Underfull \hbox (badness 10000) in paragraph at lines 66944--66949
[]\T1/qtm/m/n/10 Added new meth-ods [][]\T1/txtt/m/sl/10 DataEngineQueryGenerat
or.prepare_prediction_dataset[][] \T1/qtm/m/n/10 and

Underfull \hbox (badness 7308) in paragraph at lines 66944--66949
[][]\T1/txtt/m/sl/10 DataEngineQueryGenerator.prepare_prediction_dataset_from_c
atalog[][] \T1/qtm/m/n/10 that ap-ply time se-ries
[658]
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[]\T1/qtm/m/n/10 Added two new meth-ods to the Im-ageAug-men-ta-tion-List class
: [][]\T1/txtt/m/sl/10 ImageAugmentationList.list[][]\T1/qtm/m/n/10 ,
[659]
Underfull \hbox (badness 10000) in paragraph at lines 67080--67084
[]\T1/qtm/m/n/10 Re-stored [][]\T1/txtt/m/sl/10 Model.get_leaderboard_ui_permal
ink[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 Model.open_model_browser[][]\T1/qt
m/m/n/10 , [][]\T1/txtt/m/sl/10 Project.
[660] [661]
Underfull \hbox (badness 10000) in paragraph at lines 67330--67333
[]\T1/qtm/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/qtm/m/n/10 Added in-for-ma-tion about ver-tex to ad-vanced tun-ing pa-ram
-e-ters [][]\T1/txtt/m/sl/10 datarobot.models.Model.
[662]
Underfull \hbox (badness 10000) in paragraph at lines 67418--67420
[][][]\T1/txtt/m/sl/10 CustomModelVersion[][]\T1/qtm/m/n/10 ’s \T1/txtt/m/n/1
0 required_metadata \T1/qtm/m/n/10 pa-ram-e-ter has been re-moved. Use

Underfull \hbox (badness 10000) in paragraph at lines 67422--67424
[][][]\T1/txtt/m/sl/10 CustomTaskVersion[][]\T1/qtm/m/n/10 ’s \T1/txtt/m/n/10
 required_metadata \T1/qtm/m/n/10 pa-ram-e-ter has been re-moved. Use

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[]\T1/qtm/m/n/10 The [][]\T1/txtt/m/sl/10 Deployment[][] \T1/qtm/m/n/10 method 
\T1/txtt/m/n/10 create_from_custom_model_image \T1/qtm/m/n/10 was re-moved. Use
 [][]\T1/txtt/m/sl/10 Deployment.
[663]
Underfull \hbox (badness 10000) in paragraph at lines 67506--67511
[]\T1/qtm/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-

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\T1/qtm/m/n/10 ing: - [][]\T1/txtt/m/sl/10 DocumentTextExtractionSample[][] \T1
/qtm/m/n/10 - [][]\T1/txtt/m/sl/10 DocumentTextExtractionSamplePage[][] \T1/qtm
/m/n/10 -

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[]\T1/qtm/m/n/10 Added sup-port to pass cal-cu-la-tion mode to pre-dic-tion ex-
pla-na-tions (\T1/qtm/m/it/10 mode \T1/qtm/m/n/10 pa-ram-e-ter in

Underfull \hbox (badness 10000) in paragraph at lines 67599--67612
\T1/qtm/m/n/10 - [][]\T1/txtt/m/sl/10 Project.apply_bias_mitigation[][] \T1/qtm
/m/n/10 - [][]\T1/txtt/m/sl/10 Project.request_bias_mitigation_feature_info[][]


Underfull \hbox (badness 10000) in paragraph at lines 67599--67612
\T1/qtm/m/n/10 - [][]\T1/txtt/m/sl/10 Project.get_bias_mitigation_feature_info[
][] \T1/qtm/m/n/10 and by adding new bias mit-

Underfull \hbox (badness 10000) in paragraph at lines 67599--67612
\T1/qtm/m/n/10 i-ga-tion params - bias_mitigation_feature_name - bias_mitigatio
n_technique - in-

Underfull \hbox (badness 10000) in paragraph at lines 67599--67612
\T1/qtm/m/n/10 clude_bias_mitigation_feature_as_predictor_variable to the ex-is
t-ing method - [][]\T1/txtt/m/sl/10 Project.start[][] \T1/qtm/m/n/10 and

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\T1/qtm/m/n/10 by adding this enum to sup-ply params to some of the above func-
tion-al-ity \T1/txtt/m/n/10 datarobot.enums.
[664] [665]
Underfull \hbox (badness 10000) in paragraph at lines 67725--67727
[]\T1/qtm/m/n/10 Up-date the doc-u-men-ta-tion to sug-gest that \T1/qtm/m/it/10
 fea-ture_derivation_window_end \T1/qtm/m/n/10 of [][]\T1/txtt/m/sl/10 datarobo
t.

Underfull \hbox (badness 10000) in paragraph at lines 67743--67746
[]\T1/qtm/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
[666] [667]
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[]

Underfull \hbox (badness 10000) in paragraph at lines 67955--67956


Underfull \hbox (badness 10000) in paragraph at lines 67969--67971
[]\T1/txtt/m/n/10 DatetimeModel.get_feature_fit <datarobot.models.DatetimeModel
.

Underfull \hbox (badness 10000) in paragraph at lines 67995--67998
[]\T1/qtm/m/n/10 Dep-re-cat-ing sca-le-out pa-ram-e-ters for projects / mod-els
. In-cludes \T1/txtt/m/n/10 scaleout_modeling_mode\T1/qtm/m/n/10 ,
[668]
Underfull \hbox (badness 6268) in paragraph at lines 68096--68099
[]\T1/qtm/m/n/10 Added the abil-ity to up-load a pre-dic-tion dataset into a pr
oject from the AI cat-a-log [][]\T1/txtt/m/sl/10 Project.

Underfull \hbox (badness 10000) in paragraph at lines 68131--68135
[]\T1/qtm/m/n/10 Added the abil-ity to con-trol class map-ping ag-gre-ga-tion i
n mul-ti-class projects via

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
[]\T1/qtm/m/n/10 Added the abil-ity to com-pute and re-trieve Fea-ture Ef-fects
 for a Mul-ti-class model us-

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
\T1/qtm/m/n/10 ing [][]\T1/txtt/m/sl/10 datarobot.models.Model.request_feature_
effects_multiclass()[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 datarobot.

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
\T1/txtt/m/sl/10 models.Model.get_feature_effects_multiclass()[][] \T1/qtm/m/n/
10 or [][]\T1/txtt/m/sl/10 datarobot.models.Model.

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
\T1/txtt/m/sl/10 get_or_request_feature_effects_multiclass()[][] \T1/qtm/m/n/10
 meth-ods. For date-time mod-els use fol-low-

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
\T1/qtm/m/n/10 ing meth-ods [][]\T1/txtt/m/sl/10 datarobot.models.DatetimeModel
.request_feature_effects_multiclass()[][]\T1/qtm/m/n/10 ,

Underfull \hbox (badness 10000) in paragraph at lines 68141--68149
[][]\T1/txtt/m/sl/10 datarobot.models.DatetimeModel.get_feature_effects_multicl
ass()[][] \T1/qtm/m/n/10 or [][]\T1/txtt/m/sl/10 datarobot.models.
[669] [670]
Underfull \hbox (badness 10000) in paragraph at lines 68291--68294
[]\T1/qtm/m/n/10 fix [][]\T1/txtt/m/sl/10 datarobot.CustomTaskVersion[][] \T1/q
tm/m/n/10 and [][]\T1/txtt/m/sl/10 datarobot.CustomModelVersion[][] \T1/qtm/m/n
/10 to cor-rectly for-mat

Underfull \hbox (badness 10000) in paragraph at lines 68370--68373
[]\T1/qtm/m/n/10 Added the abil-ity to re-quest a spe-cific model be pre-pared 
for de-ploy-ment us-ing [][]\T1/txtt/m/sl/10 Project.
[671] [672] [673]
Underfull \hbox (badness 10000) in paragraph at lines 68611--68614
[]\T1/qtm/m/n/10 The \T1/txtt/m/n/10 required_metadata \T1/qtm/m/n/10 prop-erty
 of [][]\T1/txtt/m/sl/10 datarobot.CustomModelVersion[][] \T1/qtm/m/n/10 has be
en dep-re-cated.

Underfull \hbox (badness 10000) in paragraph at lines 68616--68619
[]\T1/qtm/m/n/10 The \T1/txtt/m/n/10 required_metadata \T1/qtm/m/n/10 prop-erty
 of [][]\T1/txtt/m/sl/10 datarobot.CustomTaskVersion[][] \T1/qtm/m/n/10 has bee
n dep-re-cated.
[674]
Underfull \hbox (badness 7907) in paragraph at lines 68787--68792
[]\T1/qtm/m/n/10 Sup-port for Batch Pre-dic-tion Job Def-i-ni-tions has now bee
n added through the fol-low-ing class:

Underfull \hbox (badness 10000) in paragraph at lines 68829--68832
[]\T1/qtm/m/n/10 Added method to down-load fea-ture dis-cov-ery recipe SQLs (li
m-ited beta fea-ture). [][]\T1/txtt/m/sl/10 Project.
[675]
Underfull \hbox (badness 10000) in paragraph at lines 68905--68910
[]\T1/qtm/m/n/10 Par-tial his-tory pre-dic-tions can be made with time se-ries 
time se-ries mul-ti-series mod-els us-

Underfull \hbox (badness 10000) in paragraph at lines 68905--68910
\T1/qtm/m/n/10 ing the \T1/txtt/m/n/10 allow_partial_history_time_series_predic
tions \T1/qtm/m/n/10 at-tribute of the [][]\T1/txtt/m/sl/10 datarobot.

Underfull \hbox (badness 10000) in paragraph at lines 68930--68938
[][]\T1/txtt/m/sl/10 PairwiseJointProbabilities[][] \T1/qtm/m/n/10 and [][]\T1/
txtt/m/sl/10 PairwiseConditionalProbabilities[][] \T1/qtm/m/n/10 or [][]\T1/txt
t/m/sl/10 Feature.

Underfull \hbox (badness 10000) in paragraph at lines 68930--68938
\T1/txtt/m/sl/10 get_pairwise_correlations[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/s
l/10 Feature.get_pairwise_joint_probabilities[][] \T1/qtm/m/n/10 and [][]\T1/tx
tt/m/sl/10 Feature.

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[]

Underfull \hbox (badness 10000) in paragraph at lines 68965--68966

[676]
Underfull \hbox (badness 10000) in paragraph at lines 69001--69004
[]\T1/qtm/m/n/10 Sup-port for down-load-ing fea-ture dis-cov-ery train-ing or p
re-dic-tion dataset us-ing [][]\T1/txtt/m/sl/10 Project.

Underfull \hbox (badness 10000) in paragraph at lines 69006--69012
[]\T1/qtm/m/n/10 Added [][]\T1/txtt/m/sl/10 datarobot.models.FeatureAssociation
Matrix[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 datarobot.models.

Underfull \hbox (badness 10000) in paragraph at lines 69006--69012
\T1/txtt/m/sl/10 FeatureAssociationMatrixDetails[][] \T1/qtm/m/n/10 and [][]\T1
/txtt/m/sl/10 datarobot.models.FeatureAssociationFeaturelists[][]

Underfull \hbox (badness 7168) in paragraph at lines 69031--69034
[]\T1/qtm/m/n/10 Re-move \T1/qtm/m/it/10 de-sired_memory \T1/qtm/m/n/10 param f
rom the fol-low-ing classes: [][]\T1/txtt/m/sl/10 datarobot.CustomInferenceMode
l[][]\T1/qtm/m/n/10 ,

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[]\T1/qtm/m/n/10 Re-move \T1/txtt/m/n/10 desired_memory \T1/qtm/m/n/10 param fr
om the fol-low-ing meth-ods: [][]\T1/txtt/m/sl/10 CustomInferenceModel.create[]
[]\T1/qtm/m/n/10 ,

Underfull \hbox (badness 10000) in paragraph at lines 69036--69043
[][]\T1/txtt/m/sl/10 CustomModelVersion.create_clean[][]\T1/qtm/m/n/10 , [][]\T
1/txtt/m/sl/10 CustomModelVersion.create_clean[][]\T1/qtm/m/n/10 , [][]\T1/txtt
/m/sl/10 CustomModelTest.
[677]
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[]

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[]

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[]\T1/qtm/m/n/10 Fea-ture dis-cov-ery projects can now be cre-ated us-ing the [
][]\T1/txtt/m/sl/10 Project.start[][] \T1/qtm/m/n/10 method by pro-vid-ing

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[]

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[678]
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[]\T1/qtm/m/n/10 New \T1/txtt/m/n/10 sampling_method \T1/qtm/m/n/10 param in []
[]\T1/txtt/m/sl/10 Model.train_datetime[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/1
0 Project.train_datetime[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 Model.

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[]

Overfull \hbox (1.81398pt too wide) in paragraph at lines 69276--69279
[][][]\T1/txtt/m/sl/10 SecondaryDatasetConfigurations.

Underfull \hbox (badness 10000) in paragraph at lines 69283--69285
[]\T1/qtm/m/n/10 Class \T1/txtt/m/n/10 FeatureEngineeringGraph \T1/qtm/m/n/10 h
as been re-moved. Use [][]\T1/txtt/m/sl/10 datarobot.models.
[679]
Underfull \hbox (badness 10000) in paragraph at lines 69300--69303
[]\T1/qtm/m/n/10 for [][]\T1/txtt/m/sl/10 datarobot.

Underfull \hbox (badness 10000) in paragraph at lines 69307--69310
[]\T1/qtm/m/n/10 for [][]\T1/txtt/m/sl/10 datarobot.models.

Underfull \hbox (badness 10000) in paragraph at lines 69314--69317
[][][]\T1/txtt/m/sl/10 datarobot.

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[]\T1/qtm/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:
[680] [681]
Underfull \hbox (badness 10000) in paragraph at lines 69554--69563
[]\T1/qtm/m/n/10 Added new ar-gu-ments \T1/txtt/m/n/10 explanation_algorithm \T
1/qtm/m/n/10 and \T1/txtt/m/n/10 max_explanations \T1/qtm/m/n/10 to method [][]
\T1/txtt/m/sl/10 Model.

Underfull \hbox (badness 10000) in paragraph at lines 69565--69572
[]\T1/qtm/m/n/10 Added new ar-gu-ments \T1/txtt/m/n/10 explanation_algorithm \T
1/qtm/m/n/10 and \T1/txtt/m/n/10 max_explanations \T1/qtm/m/n/10 to method [][]
\T1/txtt/m/sl/10 Model.
[682] [683]
Underfull \hbox (badness 10000) in paragraph at lines 69765--69769
[]\T1/qtm/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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[]

Underfull \hbox (badness 10000) in paragraph at lines 69771--69772


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[]\T1/qtm/m/n/10 These meth-ods now pro-vide ad-di-tional meta-data fields in F
ea-ture Im-pact re-sults if called with

Underfull \hbox (badness 10000) in paragraph at lines 69823--69827
[]\T1/qtm/m/n/10 Sec-ondary dataset con-fig-u-ra-tion re-trieve and dele-tion i
s eas-ier now though new

Underfull \hbox (badness 10000) in paragraph at lines 69823--69827
[][]\T1/txtt/m/sl/10 SecondaryDatasetConfigurations.delete[][] \T1/qtm/m/n/10 s
oft deletes a Sec-ondary dataset con-fig-u-ra-tion.
[684]
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[]

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[685]
Underfull \hbox (badness 10000) in paragraph at lines 70093--70097
[]\T1/qtm/m/n/10 A new boolean at-tribute \T1/txtt/m/n/10 unsupervised_mode \T1
/qtm/m/n/10 was added to [][]\T1/txtt/m/sl/10 datarobot.
[686]
Underfull \hbox (badness 10000) in paragraph at lines 70173--70177
[]\T1/qtm/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 70173--70177
[][]\T1/txtt/m/sl/10 Deployment.get_prediction_intervals_settings[][] \T1/qtm/m
/n/10 and [][]\T1/txtt/m/sl/10 Deployment.
[687]
Underfull \hbox (badness 10000) in paragraph at lines 70226--70229
[]\T1/qtm/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/txtt/m/sl/10 Project.

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[]\T1/qtm/m/n/10 Method [][]\T1/txtt/m/sl/10 Project.set_target[][] \T1/qtm/m/n
/10 sup-port new op-tional pa-ram-e-ters \T1/txtt/m/n/10 featureEngineeringGrap
hs \T1/qtm/m/n/10 and

Underfull \hbox (badness 10000) in paragraph at lines 70271--70276
[]\T1/qtm/m/n/10 Se-ries ac-cu-racy re-trieval meth-ods ([][]\T1/txtt/m/sl/10 D
atetimeModel.get_series_accuracy_as_dataframe[][] \T1/qtm/m/n/10 and
[688]
Underfull \hbox (badness 10000) in paragraph at lines 70353--70355
[]\T1/qtm/m/n/10 Mono-tonic con-straints are now sup-ported for OTV projects. T
o that end, the pa-ram-e-ters

Underfull \hbox (badness 10000) in paragraph at lines 70353--70355
\T1/txtt/m/n/10 monotonic_increasing_featurelist_id \T1/qtm/m/n/10 and \T1/txtt
/m/n/10 monotonic_decreasing_featurelist_id \T1/qtm/m/n/10 can be
[689] [690] [691]
Underfull \hbox (badness 10000) in paragraph at lines 70643--70652
[]\T1/qtm/m/n/10 Pre-vi-ously avail-able for only Eu-reqa mod-els, Ad-vanced Tu
n-ing meth-ods and ob-jects, in-clud-ing

Underfull \hbox (badness 10000) in paragraph at lines 70643--70652
[][]\T1/txtt/m/sl/10 Model.start_advanced_tuning_session[][]\T1/qtm/m/n/10 , []
[]\T1/txtt/m/sl/10 Model.get_advanced_tuning_parameters[][]\T1/qtm/m/n/10 , [][
]\T1/txtt/m/sl/10 Model.

Underfull \hbox (badness 10000) in paragraph at lines 70682--70684
[][][]\T1/txtt/m/sl/10 Project.upload_dataset[][] \T1/qtm/m/n/10 and [][]\T1/tx
tt/m/sl/10 Project.upload_dataset_from_data_source[][] \T1/qtm/m/n/10 will re-t
urn a
[692] [693]
Underfull \hbox (badness 10000) in paragraph at lines 70807--70810
[]\T1/qtm/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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s mul-ti-series projects us-ing

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\T1/qtm/m/n/10 the \T1/txtt/m/n/10 use_cross_series_features \T1/qtm/m/n/10 and
 \T1/txtt/m/n/10 aggregation_type \T1/qtm/m/n/10 at-tributes of the [][]\T1/txt
t/m/sl/10 datarobot.

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[]\T1/qtm/m/n/10 New \T1/txtt/m/n/10 number_of_known_in_advance_features \T1/qt
m/m/n/10 at-tribute has been added to the [][]\T1/txtt/m/sl/10 datarobot.
[694]
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[]\T1/qtm/m/n/10 Train-ing pre-dic-tions for date-time par-ti-tioned projects n
ow sup-port the new data sub-set

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[]\T1/qtm/m/n/10 New meth-ods and ob-jects, in-clud-ing [][]\T1/txtt/m/sl/10 Mo
del.start_advanced_tuning_session[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 Mode
l.

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\T1/txtt/m/sl/10 get_advanced_tuning_parameters[][]\T1/qtm/m/n/10 , [][]\T1/txt
t/m/sl/10 Model.advanced_tune[][]\T1/qtm/m/n/10 , and [][]\T1/txtt/m/sl/10 Adva
ncedTuningSession[][]\T1/qtm/m/n/10 , were

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[]\T1/qtm/m/n/10 In-di-vid-ual cross val-i-da-tion scores can be re-trieved for
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[][][]\T1/txtt/m/sl/10 ModelRecommendation.get[][] \T1/qtm/m/n/10 now ac-cepts 
an op-tional pa-ram-e-ter of type \T1/txtt/m/n/10 datarobot.enums.

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-t” has been added to
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[]\T1/qtm/m/n/10 Added a new fea-ture to re-trieve fea-ture logs for time se-ri
es projects. Check [][]\T1/txtt/m/sl/10 datarobot.

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\T1/txtt/m/sl/10 DatetimePartitioning.feature_log_list()[][] \T1/qtm/m/n/10 and
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[697] [698] [699] [700] [701] [702] [703] [704] [705]
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[]\T1/qtm/m/n/10 its
[706] [707] [708] [709]
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[]\T1/qtm/m/n/10 In sev-eral places where \T1/txtt/m/n/10 AppPlatformError \T1/
qtm/m/n/10 was be-ing raised, now \T1/txtt/m/n/10 TypeError\T1/qtm/m/n/10 , \T1
/txtt/m/n/10 ValueError \T1/qtm/m/n/10 or

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[]\T1/qtm/m/n/10 If a \T1/txtt/m/n/10 ClientError \T1/qtm/m/n/10 or \T1/txtt/m/
n/10 ServerError \T1/qtm/m/n/10 oc-curs dur-ing a call to \T1/txtt/m/n/10 Proje
ct.from_async\T1/qtm/m/n/10 , then a
[710]
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[]\T1/txtt/m/n/10 recommendation_settings \T1/qtm/m/n/10 pa-ram-e-ter of \T1/tx
tt/m/n/10 Project.start \T1/qtm/m/n/10 is dep-re-cated in fa-vor of
[711] [712] [713] [714] [715] (./datarobot-public-api-client.ind [716]
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[]\T1/txtt/m/n/10 AccuracyOverTime \T1/qtm/m/n/10 (\T1/qtm/m/it/10 class in

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lass in

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[]\T1/txtt/m/n/10 activate() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.D
eployment method\T1/qtm/m/n/10 ),

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dels.user_blueprints.models.UserBlueprintAddToProjectMenu

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erBlueprint class

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egmentationTask

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taEngineQueryGenerator

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nEnvironmentVersion

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atchPredictionJob

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odels.Predictions

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n

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in

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ssmentPredictionsPreview

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lass in

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 class in

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
api_object.APIObject

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[]\T1/txtt/m/n/10 from_data() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.
BlenderModel class

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Blueprint class

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DatetimeModel class

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Deployment class

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Featurelist class

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PayoffMatrix class

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PrimeModel class

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RatingTableModel

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segmentation.SegmentationTask

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ationTask class

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redictJob class
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CustomTask class

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CustomTaskVersion

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DataSource class

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DataStore class

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models.api_object.APIObject

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models.Blueprint

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models.Deployment

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models.Featurelist

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models.pareto_front.ParetoFront

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models.PayoffMatrix

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Partitioning class

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utomated_documentation.AutomatedDocument

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.models.model_lineage.ModelLineage

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y_assessment.AnomalyAssessmentExplanations

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redictionJob class

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ModelVersionConversion

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ment.Accuracy class

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ment.bias_and_fairness.FairnessScoresOverTime

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ment.ServiceStatsOverTime

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eAssociationMatrixDetails

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eHistogram class

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eImpactJob class

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g_report.MissingValuesReport

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lueprintChart class

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ngFeature class

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ngFeaturelist class

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seConditionalProbabilities

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seCorrelations class

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seJointProbabilities

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tionDataset class

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TableModel class

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onshipsConfiguration

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ng_predictions.TrainingPredictions

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ai.ImageAugmentationOptions

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lanationsInitialization

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alendarFile class

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ataStore method\T1/qtm/m/n/10 ),

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odels.Project

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delRecommendation

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models.Dataset

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ot.models.Model

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models.Dataset

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models.deployment.AccuracyOverTime

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stomModelVersionDependencyBuild

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cutionEnvironmentVersion

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ProjectLocation prop-

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10 in mod-ule

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in mod-ule

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ot.models.Job

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[]\T1/txtt/m/n/10 WordCloudNgram \T1/qtm/m/n/10 (\T1/qtm/m/it/10 class in
[742]) (./datarobot-public-api-client.aux) )
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Output written on datarobot-public-api-client.pdf (746 pages, 3778014 bytes).
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Latexmk: Log file says output to 'datarobot-public-api-client.pdf'
Latexmk: Errors, in force_mode: so I tried finishing targets
Collected error summary (may duplicate other messages):
  pdflatex: Command for 'pdflatex' gave return code 256