Read the Docs build information
Build id: 1841564
Project: datarobot-public-api-client
Version: early-access
Commit: d324e6ad088301335dcff0623bbada8028c6cf97
Date: 2023-11-13T16:47:19.650042Z
State: finished
Success: True


[rtd-command-info] start-time: 2023-11-13T16:47:20.823335Z, end-time: 2023-11-13T16:47:27.818347Z, duration: 6, exit-code: 0
git clone --depth 1 git@github.com:datarobot/public_api_client.git .
Cloning into '.'...
Warning: Permanently added 'github.com' (ED25519) to the list of known hosts.

[rtd-command-info] start-time: 2023-11-13T16:47:27.878849Z, end-time: 2023-11-13T16:47:29.225250Z, duration: 1, exit-code: 0
git fetch origin --force --prune --prune-tags --depth 50 refs/tags/early-access:refs/tags/early-access
Warning: Permanently added 'github.com' (ED25519) to the list of known hosts.

[rtd-command-info] start-time: 2023-11-13T16:47:29.356245Z, end-time: 2023-11-13T16:47:30.035980Z, duration: 0, exit-code: 0
git checkout --force d324e6ad088301335dcff0623bbada8028c6cf97
Note: switching to 'd324e6ad088301335dcff0623bbada8028c6cf97'.

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 d324e6a [BUZOK-1197] Update LLMBlueprint string repr and fix genai_example (#2815)

[rtd-command-info] start-time: 2023-11-13T16:47:30.102107Z, end-time: 2023-11-13T16:47:30.160846Z, duration: 0, exit-code: 0
git clean -d -f -f


[rtd-command-info] start-time: 2023-11-13T16:47:30.268957Z, end-time: 2023-11-13T16:47:30.334792Z, duration: 0, exit-code: 0
cat .readthedocs.yml
# .readthedocs.yml
# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details

# Required
version: 2

# Build documentation in the docs/ directory with Sphinx
sphinx:
  configuration: sdk_docs/conf.py

# Build documentation with MkDocs
#mkdocs:
#  configuration: mkdocs.yml

# Optionally build your docs in additional formats such as PDF
formats:
  - pdf

# Optionally set the version of Python and requirements required to build your docs
python:
  install:
    - method: pip
      path: .
      extra_requirements:
        - dev

build:
  os: ubuntu-22.04
  tools:
    python: "3.7"

[rtd-command-info] start-time: 2023-11-13T16:47:36.313608Z, end-time: 2023-11-13T16:47:36.388212Z, duration: 0, exit-code: 0
asdf global python 3.7.17


[rtd-command-info] start-time: 2023-11-13T16:47:36.929150Z, end-time: 2023-11-13T16:47:38.207175Z, duration: 1, exit-code: 0
python -mvirtualenv $READTHEDOCS_VIRTUALENV_PATH
created virtual environment CPython3.7.17.final.0-64 in 939ms
  creator CPython3Posix(dest=/home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/early-access, clear=False, no_vcs_ignore=False, global=False)
  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/home/docs/.local/share/virtualenv)
    added seed packages: pip==23.1, setuptools==67.6.1, wheel==0.40.0
  activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator

[rtd-command-info] start-time: 2023-11-13T16:47:38.279263Z, end-time: 2023-11-13T16:47:49.461391Z, duration: 11, 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/early-access/lib/python3.7/site-packages (23.1)
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Requirement already satisfied: setuptools in /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/envs/early-access/lib/python3.7/site-packages (67.6.1)
Collecting setuptools
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Installing collected packages: setuptools, pip
  Attempting uninstall: setuptools
    Found existing installation: setuptools 67.6.1
    Uninstalling setuptools-67.6.1:
      Successfully uninstalled setuptools-67.6.1
  Attempting uninstall: pip
    Found existing installation: pip 23.1
    Uninstalling pip-23.1:
      Successfully uninstalled pip-23.1
Successfully installed pip-23.3.1 setuptools-68.0.0

[rtd-command-info] start-time: 2023-11-13T16:47:49.538676Z, end-time: 2023-11-13T16:47:56.112205Z, duration: 6, exit-code: 0
python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
Collecting sphinx
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Installing collected packages: snowballstemmer, pytz, zipp, urllib3, typing-extensions, sphinxcontrib-serializinghtml, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, Pygments, packaging, MarkupSafe, imagesize, idna, docutils, charset-normalizer, certifi, babel, alabaster, requests, Jinja2, importlib-metadata, sphinx, readthedocs-sphinx-ext
Successfully installed Jinja2-3.1.2 MarkupSafe-2.1.3 Pygments-2.16.1 alabaster-0.7.13 babel-2.13.1 certifi-2023.7.22 charset-normalizer-3.3.2 docutils-0.19 idna-3.4 imagesize-1.4.1 importlib-metadata-6.7.0 packaging-23.2 pytz-2023.3.post1 readthedocs-sphinx-ext-2.2.3 requests-2.31.0 snowballstemmer-2.2.0 sphinx-5.3.0 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 sphinxcontrib-serializinghtml-1.1.5 typing-extensions-4.7.1 urllib3-2.0.7 zipp-3.15.0

[rtd-command-info] start-time: 2023-11-13T16:47:56.230415Z, end-time: 2023-11-13T16:48:37.360534Z, duration: 41, exit-code: 0
python -m pip install --upgrade --upgrade-strategy only-if-needed --no-cache-dir .[dev]
Processing /home/docs/checkouts/readthedocs.org/user_builds/datarobot-public-api-client/checkouts/early-access
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
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Installing collected packages: webencodings, wcwidth, types-urllib3, types-PyYAML, types-pytz, types-python-dateutil, trafaret, strenum, ptyprocess, pickleshare, jupyter_highlight_selected_word, ipython_genutils, fastjsonschema, backcall, wrapt, websocket-client, urllib3, types-requests, typed-ast, traitlets, tornado, tomlkit, tomli, tokenize-rt, tinycss2, soupsieve, sniffio, six, Send2Trash, pyzmq, pyyaml, pyrsistent, pyflakes, pycparser, pycodestyle, py, psutil, prompt-toolkit, prometheus-client, platformdirs, pkgutil-resolve-name, Pillow, pexpect, pathspec, parso, pandocfilters, numpy, nest-asyncio, mypy-extensions, mistune, mccabe, lxml, lazy-object-proxy, jupyterlab-pygments, isort, iniconfig, importlib-resources, importlib-metadata, exceptiongroup, entrypoints, docutils, dill, defusedxml, decorator, debugpy, coverage, terminado, python-dateutil, pluggy, mypy, mock, matplotlib-inline, jupyter_core, jedi, flake8, click, cffi, bleach, beautifulsoup4, attrs, astroid, anyio, Sphinx, responses, requests-toolbelt, pytest, pylint, pandas, jupyter-client, jsonschema, ipython, black, argon2-cffi-bindings, sphinx-rtd-theme, sphinx-autodoc-typehints, pytest-cov, pytest-asyncio, numpydoc, nbformat, ipykernel, datarobot, argon2-cffi, nbclient, nbconvert, nbsphinx, jupyter-server, notebook-shim, nbclassic, notebook, jupyter_contrib_core, jupyter_nbextensions_configurator, jupyter-contrib-nbextensions
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  Attempting uninstall: Sphinx
    Found existing installation: Sphinx 5.3.0
    Uninstalling Sphinx-5.3.0:
      Successfully uninstalled Sphinx-5.3.0
Successfully installed Pillow-9.2.0 Send2Trash-1.8.2 Sphinx-4.3.2 anyio-3.7.1 argon2-cffi-23.1.0 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-8.1.7 coverage-7.2.7 datarobot-3.4.0b0 debugpy-1.7.0 decorator-5.1.1 defusedxml-0.7.1 dill-0.3.7 docutils-0.17.1 entrypoints-0.4 exceptiongroup-1.1.3 fastjsonschema-2.18.1 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-0.19.1 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-4.9.3 matplotlib-inline-0.1.6 mccabe-0.7.0 mistune-3.0.2 mock-3.0.5 mypy-1.0.0 mypy-extensions-1.0.0 nbclassic-1.0.0 nbclient-0.7.4 nbconvert-7.6.0 nbformat-5.8.0 nbsphinx-0.8.9 nest-asyncio-1.5.8 notebook-6.5.6 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-0.11.2 pexpect-4.8.0 pickleshare-0.7.5 pkgutil-resolve-name-1.3.10 platformdirs-4.0.0 pluggy-1.2.0 prometheus-client-0.17.1 prompt-toolkit-3.0.40 psutil-5.9.6 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-0.21.1 pytest-cov-4.1.0 python-dateutil-2.8.2 pyyaml-6.0.1 pyzmq-24.0.1 requests-toolbelt-1.0.0 responses-0.21.0 six-1.16.0 sniffio-1.3.0 soupsieve-2.4.1 sphinx-autodoc-typehints-1.17.1 sphinx-rtd-theme-1.0.0 strenum-0.4.15 terminado-0.17.1 tinycss2-1.2.1 tokenize-rt-5.0.0 tomli-2.0.1 tomlkit-0.12.2 tornado-6.2 trafaret-2.1.1 traitlets-5.9.0 typed-ast-1.5.5 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 urllib3-1.26.18 wcwidth-0.2.9 webencodings-0.5.1 websocket-client-1.6.1 wrapt-1.16.0

[rtd-command-info] start-time: 2023-11-13T16:48:38.166536Z, end-time: 2023-11-13T16:48:38.218915Z, 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')

# Define this variable in case it's not defined by the user.
# It defaults to `alabaster` which is the default from Sphinx.
# https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_theme
html_theme = globals().get('html_theme', 'alabaster')

#Add project information to the template context.
context = {
    'html_theme': html_theme,
    'current_version': "early-access",
    'version_slug': "early-access",
    'MEDIA_URL': "https://media.readthedocs.com/media/",
    'STATIC_URL': "https://assets.readthedocs.com/",
    'PRODUCTION_DOMAIN': "readthedocs.com",
    'proxied_static_path': "/_/static/",
    'versions': [
    ("v3.2.1", "/en/v3.2.1/"),
    ("v3.2.0", "/en/v3.2.0/"),
    ("v3.2.0b1", "/en/v3.2.0b1/"),
    ("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/"),
    ("v2.28.3", "/en/v2.28.3/"),
    ("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': [ 
    ("pdf", "//datarobot-public-api-client.readthedocs-hosted.com/_/downloads/en/early-access/pdf/"),
    ],
    'subprojects': [ 
    ],
    'slug': 'datarobot-public-api-client',
    'name': u'public_api_client',
    'rtd_language': u'en',
    'programming_language': u'py',
    'canonical_url': '',
    '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': 'early-access',
    'display_github': True,
    'bitbucket_user': 'None',
    'bitbucket_repo': 'None',
    'bitbucket_version': 'early-access',
    'display_bitbucket': False,
    'gitlab_user': 'None',
    'gitlab_repo': 'None',
    'gitlab_version': 'early-access',
    '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': 'd324e6ad',
}

# 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 'tag' == 'external':
    extensions.insert(1, "readthedocs_ext.external_version_warning")
    readthedocs_vcs_url = 'None'
    readthedocs_build_url = 'https://readthedocs.com/projects/datarobot-public-api-client/builds/1841564/'

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: 2023-11-13T16:48:38.283236Z, end-time: 2023-11-13T16:49:34.359858Z, duration: 56, 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
[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
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 43 source files that are out of date
updating environment: [new config] 43 added, 0 changed, 0 removed
reading sources... [  2%] CHANGES
reading sources... [  4%] autodoc/api_reference
You have imported from the _experimental directory.
This directory is used for unreleased datarobot features.
Unless you specifically know better, you don't have the access to use this functionality in the app, so this code will not work.
reading sources... [  6%] examples/index
reading sources... [  9%] getting_started
reading sources... [ 11%] index
reading sources... [ 13%] reference/admin/credentials
reading sources... [ 16%] reference/admin/index
reading sources... [ 18%] reference/admin/sharing
reading sources... [ 20%] reference/data/database_connectivity
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WARNING: error while formatting arguments for datarobot.models.anomaly_assessment.AnomalyAssessmentRecord: __name__
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dumping object inventory... done
build succeeded, 3 warnings.

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

[rtd-command-info] start-time: 2023-11-13T16:49:34.467867Z, end-time: 2023-11-13T16:50:31.650131Z, duration: 57, 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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You have imported from the _experimental directory.
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WARNING: error while formatting arguments for datarobot.models.anomaly_assessment.AnomalyAssessmentRecord: __name__
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checking consistency... done
processing DataRobotPython_3.4.0b0_Docs.tex... index getting_started reference/index reference/data/index reference/data/dataset reference/data/database_connectivity reference/data/feature_discovery reference/modeling/index reference/modeling/project reference/modeling/model reference/modeling/blueprint reference/modeling/spec/index reference/modeling/spec/datetime_partition reference/modeling/spec/time_series reference/modeling/spec/visualai reference/modeling/spec/unsupervised_anomaly reference/modeling/spec/unsupervised_clustering reference/modeling/spec/segmented_modeling reference/modeling/spec/custom_task reference/modeling/spec/monotonic_constraints reference/modeling/spec/binary_data reference/modeling/insights/index reference/modeling/insights/prediction_explanations reference/modeling/insights/rating_table reference/modeling/insights/automated_documentation reference/modeling/insights/external_testset reference/modeling/job reference/modeling/prime reference/modeling/model_recommendation reference/predictions/index reference/predictions/predict_job reference/predictions/batch_predictions reference/mlops/index reference/mlops/deployment reference/mlops/custom_model reference/admin/index reference/admin/credentials reference/admin/sharing reference/use_cases/index reference/use_cases/use_cases autodoc/api_reference examples/index CHANGES 
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(use `make latexpdf' here to do that automatically).
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[rtd-command-info] start-time: 2023-11-13T16:50:32.500822Z, end-time: 2023-11-13T16:50:32.558482Z, 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: 2023-11-13T16:50:32.625359Z, end-time: 2023-11-13T16:51:00.817900Z, duration: 28, exit-code: 0
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Chapter 1.
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[]\T1/qtm/m/n/10 For mul-ti-class mod-els, you can get a list of per-class lift
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/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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[59] [60] [61 <./sample1.png> <./sample2.png>] [62] [63 <./activation_map1.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
[70]
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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
[71] [72] [73] [74] [75] [76] [77] [78]

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l.5151 ...gment\PYGZus{}id}\PYG{l+s+s2}{\PYGZdq{}}
                                                  

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l.5151 ...gment\PYGZus{}id}\PYG{l+s+s2}{\PYGZdq{}}
                                                  
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l.5758     ├── images
                           

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l.5762     ├── data.csv
                             

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l.5762     ├── data.csv
                             

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l.5762     ├── data.csv
                             
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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
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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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\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
Parity\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
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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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[]\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 par-ti-tion : sub-set of train-ing data to score, one of \T1/t
xtt/m/n/10 datarobot.enums.
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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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RESS_POLICY.NONE\T1/qtm/m/n/10 ,

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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 - for \T1/qtm/m/it/10 query \T1/qtm/m/n/10 based data-sources - 
`[][]$https : / / s3 . amazonaws . com / datarobot _ test /
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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 {`key':'DataRobot', `sum-mary':{`min':0, `max':29815.0, `std-D
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m. The
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nd 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-f
aults 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
[370]
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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.
[371] [372]
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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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[393] [394] [395] [396] [397]
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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 ).
[399]
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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
[400] [401] [402]
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[]\T1/qtm/m/n/10 re-sponse

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[]
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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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[]
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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[405] [406] [407] [408] [409]
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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
[410] [411] [412] [413]
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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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[]

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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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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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[]
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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
[440] [441] [442]
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[]\T1/qtm/m/n/10 re-sponse

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[]
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[]
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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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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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[]
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[]
[463]
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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[464] [465]
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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
[466] [467]
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[]\T1/qtm/m/n/10 re-sponse

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[]
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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 ).
[471]
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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
[472] [473] [474]
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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
[479]
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[]\T1/qtm/m/n/10 The back-test

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[]\T1/qtm/m/n/10 The back-test
[480]
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[]\T1/qtm/m/n/10 The back-test
[481] [482] [483] [484] [485] [486]
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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
[487]
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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
[488] [489]
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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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[]

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[]
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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
[495] [496] [497] [498] [499] [500] [501]
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[]
[502]
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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[503]
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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]
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[]\T1/qtm/m/n/10 re-sponse

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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
[508] [509] [510] [511] [512] [513] [514] [515] [516] [517] [518]
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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
[519] [520] [521] [522]
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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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[]

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[]
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[]
[529]
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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[530] [531]
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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
[532] [533]
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[]\T1/qtm/m/n/10 re-sponse

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[]
[534]
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[]

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[]
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[]
[536] [537]
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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
[538] [539] [540]
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[]\T1/qtm/m/n/10 [dict] ^^PPa-ram-e-ters^^Q dic-tio-nary, same as spec-i-fied o
n

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[]

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[541] [542] [543] [544] [545] [546]
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\T1/qtm/m/it/10 low_partial_history_time_series_predictions=False\T1/qtm/m/n/10
 , 
[547]
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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

[548] [549]
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[]\T1/qtm/m/n/10 (New in ver-sion v2.24)
[550]
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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 ,
[551]
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[]\T1/qtm/m/n/10 [bool][Switch to us-ing the older a_priori key name in-stead o
f
[552] [553]
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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
[554] [555]
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[]\T1/qtm/m/n/10 (New in ver-sion v2.24)
[556] [557] [558]
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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 ,
[559]
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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.
[560] [561] [562]
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[]\T1/qtm/m/n/10 [pandas.Dataframe] the back-test at-tributes, for-mat-ted into
 a
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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.
[573]
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[]\T1/qtm/m/n/10 Cre-ate pre-dic-tion ex-pla-na-tions for the the dataset used 
to train the model. This can be

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\T1/qtm/m/n/10 re-trieved by call-ing \T1/txtt/m/n/10 dr.Model.get().featurelis
t_id\T1/qtm/m/n/10 . For OTV and time-series projects,

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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.
[574] [575] [576]
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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 )|  
[577]
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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
[587] [588] [589] [590] [591]
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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.
[592]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
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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.
[595] [596]
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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.
[597] [598] [599] [600] [601]
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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
[602] [603] [604] [605] [606] [607] [608] [609] [610] [611] [612]
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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 ).
[613]
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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
[614]
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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 [str] Cho-sen blend method, one from \T1/txtt/m/n/10 datarobot
.enums.
[615]
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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
[616]
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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
[617]
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[]\T1/qtm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
[618] [619]
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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
[620]
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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

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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[621] [622]
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[]\T1/qtm/m/n/10 [PartitioningMethod, op-tional] An in-stance of \T1/txtt/m/n/1
0 datarobot.
[623] [624]
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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.
[625] [626] [627] [628]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[629]
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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
[630] [631] [632] [633] [634] [635] [636] [637] [638]
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[]

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

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

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

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

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\T1/qtm/m/it/10 tion_environment=None\T1/txtt/m/n/10 )|  
[642] [643] [644] [645] [646] [647] [648] [649] [650] [651] [652]
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[]\T1/qtm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
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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/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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[]\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 The role of 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 , 
[673]
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[]
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[686] [687] [688] [689] [690] [691]
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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 tual_target_value=None\T1/qtm/m/n/10 , 
[692]
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\T1/qtm/m/it/10 ment_page_width\T1/qtm/m/n/10 , 

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

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\T1/qtm/m/it/10 tual_target_value=None\T1/qtm/m/n/10 , 
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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 , 
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[]\T1/qtm/m/n/10 The back-test
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\T1/qtm/m/it/10 chunk_overlap_percentage\T1/qtm/m/n/10 , 
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[]\T1/qtm/m/it/10 embedding_models\T1/qtm/m/n/10 , 

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[]\T1/qtm/m/n/10 Set-tings for the
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[]\T1/qtm/m/n/10 The new vec-tor

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[]\T1/qtm/m/n/10 Whether to re-move the vec-tor
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[]\T1/qtm/m/n/10 Set-tings for the
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[]\T1/qtm/m/it/10 id\T1/qtm/m/n/10 , 

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

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[720]
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\T1/qtm/m/it/10 ation_user_id\T1/txtt/m/n/10 )|  
[721] [722] [723]
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\T1/txtt/m/sl/10 create_for_external[][] \T1/qtm/m/n/10 to cre-ate a new reg-is
-tered model ver-sion from an ex-ter-nal model.

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[][]\T1/txtt/m/sl/10 RegisteredModelVersion.list_associated_deployments[][] \T1
/qtm/m/n/10 to list de-ploy-ments as-so-ci-ated with

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\T1/qtm/m/n/10 a reg-is-tered model ver-sion. [][]\T1/txtt/m/sl/10 RegisteredMo
delVersion.create_for_leaderboard_item[][] \T1/qtm/m/n/10 to cre-

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\T1/qtm/m/n/10 ate a new reg-is-tered model ver-sion from a Leader-board model.
 [][]\T1/txtt/m/sl/10 RegisteredModelVersion.

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[]\T1/qtm/m/n/10 Added method meth:\T1/qtm/m/it/10 Deployment.get_predictions_v
s_actuals_over_time <datarobot.models.Deployment.get_predictions_vs_actuals_ove
r_time>
[724]
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[]

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\T1/qtm/m/n/10 All in-ter-ac-tions with \T1/txtt/m/n/10 Project.advanced_option
s \T1/qtm/m/n/10 should be ex-pected to be through the
[725]
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\T1/txtt/m/sl/10 run[][] [][]BatchMonitoringJob.get_status[][] [][]BatchMonitor
ingJob.cancel[][] [][]BatchMonitoringJob.

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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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\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[][]

Underfull \hbox (badness 10000) in paragraph at lines 73855--73868
\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.
[726]
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[]\T1/qtm/m/n/10 If the user con-fig-ures the client via env vars, then set the
 env var

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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>

Underfull \hbox (badness 10000) in paragraph at lines 73955--73970
\T1/txtt/m/sl/10 request_feature_effect[][] [][]Model.get_or_request_feature_ef
fect[][] [][]Model.get_lift_chart[][]

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[][]\T1/txtt/m/sl/10 Model.get_all_lift_charts[][] [][]Model.get_residuals_char
t[][] [][]Model.get_all_residuals_charts[][]

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[][]\T1/txtt/m/sl/10 Model.request_lift_chart[][] [][]Model.request_residuals_c
hart[][] [][]Model.get_roc_curve[][] [][]Model.
[727]
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[]\T1/qtm/m/n/10 Added new method for re-triev-ing all avail-able fea-ture im-p
acts for the model :meth:

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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.
[728]
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[]\T1/qtm/m/n/10 The sliced in-sight API end-point \T1/qtm/m/it/10 GET: api/v2/
insights/<insight_name>/ \T1/qtm/m/n/10 re-turns a pag-i-nated re-

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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.

Underfull \hbox (badness 10000) in paragraph at lines 74162--74165
[]\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 74177--74180
[]\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.

Underfull \hbox (badness 10000) in paragraph at lines 74192--74195
[]\T1/qtm/m/n/10 Dep-re-cated the fol-low-ing meth-ods for re-triev-ing [][]\T1
/txtt/m/sl/10 SharingAccess[][] \T1/qtm/m/n/10 in-for-ma-tion. - [][]\T1/txtt/m
/sl/10 DataStore.
[729]
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[]

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[]

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[730]
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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 74337--74341
[][]\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 74343--74348
[]\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 74343--74348
[][]\T1/txtt/m/sl/10 DataEngineQueryGenerator.prepare_prediction_dataset_from_c
atalog[][] \T1/qtm/m/n/10 that ap-ply time se-ries

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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 ,
[731]
Underfull \hbox (badness 10000) in paragraph at lines 74479--74483
[]\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.
[732] [733] [734]
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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.

Underfull \hbox (badness 10000) in paragraph at lines 74817--74819
[][][]\T1/txtt/m/sl/10 CustomModelVersion[][]\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/txtt/m/sl/10 CustomTaskVersion[][]\T1/qtm/m/n/10 's \T1/txtt/m/n/10 r
equired_metadata \T1/qtm/m/n/10 pa-ram-e-ter has been re-moved. Use
[735]
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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.

Underfull \hbox (badness 10000) in paragraph at lines 74905--74910
[]\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-

Underfull \hbox (badness 10000) in paragraph at lines 74905--74910
\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 -
[736]
Underfull \hbox (badness 10000) in paragraph at lines 74947--74954
[]\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 74998--75011
\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 74998--75011
\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 74998--75011
\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 74998--75011
\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.
[737]
Underfull \hbox (badness 10000) in paragraph at lines 75124--75126
[]\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 75142--75145
[]\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
[738] [739]
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[]

Underfull \hbox (badness 10000) in paragraph at lines 75354--75355


Underfull \hbox (badness 10000) in paragraph at lines 75368--75370
[]\T1/txtt/m/n/10 DatetimeModel.get_feature_fit <datarobot.models.DatetimeModel
.

Underfull \hbox (badness 10000) in paragraph at lines 75394--75397
[]\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 ,
[740]
Underfull \hbox (badness 6268) in paragraph at lines 75495--75498
[]\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.
[741]
Underfull \hbox (badness 10000) in paragraph at lines 75530--75534
[]\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 75540--75548
[]\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 75540--75548
\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 75540--75548
\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 75540--75548
\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 75540--75548
\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 75540--75548
[][]\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.
[742]
Underfull \hbox (badness 10000) in paragraph at lines 75690--75693
[]\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
[743]
Underfull \hbox (badness 10000) in paragraph at lines 75769--75772
[]\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.
[744] [745]
Underfull \hbox (badness 10000) in paragraph at lines 76010--76013
[]\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 76015--76018
[]\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.
[746]
Underfull \hbox (badness 7907) in paragraph at lines 76186--76191
[]\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 76228--76231
[]\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.
[747]
Underfull \hbox (badness 10000) in paragraph at lines 76304--76309
[]\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 76304--76309
\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 76329--76337
[][]\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 76329--76337
\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.

Overfull \hbox (7.91493pt too wide) in paragraph at lines 76364--76365
[]

Underfull \hbox (badness 10000) in paragraph at lines 76364--76365

[748]
Underfull \hbox (badness 10000) in paragraph at lines 76400--76403
[]\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 76405--76411
[]\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 76405--76411
\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.

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\T1/txtt/m/sl/10 create[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 CustomModelVer
sion.create_clean[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 CustomModelVersion.c
reate_from_previous[][]\T1/qtm/m/n/10 ,
[749]
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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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[750]
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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.
[751]
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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:
[752] [753]
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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.
[754] [755]
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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.
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[]

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[757]
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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.
[758]
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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.
[759]
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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-tuses 
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
[760]
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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
[761] [762] [763]
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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
[764] [765]
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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.
[766]
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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.
[767]
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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.
[768]
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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.
[769] [770] [771] [772] [773] [774] [775] [776] [777]
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[]\T1/qtm/m/n/10 its
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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
[782]
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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
[783] [784] [785] [786] [787] (./datarobot-public-api-client.ind [788]
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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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 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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n

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

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ass in

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lass in

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 in

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[]\T1/txtt/m/n/10 AnomalyAssessmentRecordMetadata \T1/qtm/m/n/10 (\T1/qtm/m/it/
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/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BatchPredictionJob class

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

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.BatchPredictionJob class

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[]\T1/txtt/m/n/10 archive() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.Re
gisteredModel class

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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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n/10 ),

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[]\T1/txtt/m/n/10 Backtest \T1/qtm/m/n/10 (\T1/qtm/m/it/10 class in datarobot.h
elpers.partitioning_methods\T1/qtm/m/n/10 ),
[789]
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[]\T1/txtt/m/n/10 batch_features_type_transform()

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class in

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[]\T1/txtt/m/n/10 BiasMitigationFeatureInfo \T1/qtm/m/n/10 (\T1/qtm/m/it/10 cla
ss in

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[]\T1/txtt/m/n/10 bucket_sample_sizes

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\T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.models.deployment.AccuracyOverTime

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[]\T1/txtt/m/n/10 bucket_values \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.model
s.deployment.AccuracyOverTime

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[]\T1/txtt/m/n/10 bucket_values \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot.model
s.deployment.ServiceStatsOverTime

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odels.Predictions

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redictionExplanations

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0 ),

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in

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.documentai.document.FeaturesWithSamples

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ssmentPredictionsPreview

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lass in

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api_object.APIObject

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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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experimental.models.recipes.Recipe

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redictJob class

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CustomTask class

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CustomTaskVersion

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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.lift_chart.LiftChart

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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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ryGenerator class

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y_assessment.AnomalyAssessmentExplanations

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y_assessment.AnomalyAssessmentPredictionsPreview

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onitoringJob class

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redictionJob class

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redictionJobDefinition

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ModelVersionConversion

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lice.DataSlice class
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ment.bias_and_fairness.FairnessScoresOverTime

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ment.ServiceStatsOverTime

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al_baseline_validation.ExternalBaselineValidationInfo

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eAssociationMatrixDetails

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

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ategoricalHistogram

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seConditionalProbabilities

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seCorrelations class

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seJointProbabilities

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tionEnvironment class

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[][][]\T1/txtt/m/sl/10 Dataset.create_from_data_source[][] \T1/qtm/m/n/10 takes
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[]\T1/qtm/m/n/10 For mul-ti-class mod-els, you can get a list of per-class lift
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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
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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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[46] [47] [48] [49] [50]
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[]\T1/qtm/m/n/10 Enough rows of his-tor-i-cal data must be pro-vided to cover t
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timeModel.
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l.3808     ├── abyssinian
                               

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l.3808     ├── abyssinian
                               

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l.3809     │   ├── abyssinian01.jpg
                                             

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l.3809     │   ├── abyssinian01.jpg
                                             

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l.3809     │   ├── abyssinian01.jpg
                                             

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l.3809     │   ├── abyssinian01.jpg
                                             

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l.3810     │   ├── abyssinian02.jpg
                                             

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l.3810     │   ├── abyssinian02.jpg
                                             

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l.3810     │   ├── abyssinian02.jpg
                                             

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l.3810     │   ├── abyssinian02.jpg
                                             

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l.3811     │   ├── …
                                

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l.3812     ├── american\PYGZus{}bulldog
                                             

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l.3812     ├── american\PYGZus{}bulldog
                                             

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l.3812     ├── american\PYGZus{}bulldog
                                             

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l.3813 ...├── american\PYGZus{}bulldog01.jpg
                                                  

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l.3813 ...├── american\PYGZus{}bulldog01.jpg
                                                  

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l.3813 ...├── american\PYGZus{}bulldog01.jpg
                                                  

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l.3813 ...├── american\PYGZus{}bulldog01.jpg
                                                  

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l.3814 ...├── american\PYGZus{}bulldog02.jpg
                                                  

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l.3814 ...├── american\PYGZus{}bulldog02.jpg
                                                  

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l.3814 ...├── american\PYGZus{}bulldog02.jpg
                                                  

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l.3814 ...├── american\PYGZus{}bulldog02.jpg
                                                  

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l.3815     │   ├── …
                              
[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
[70]
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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
[71] [72] [73] [74] [75] [76] [77] [78]

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l.5151 ...gment\PYGZus{}id}\PYG{l+s+s2}{\PYGZdq{}}
                                                  

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l.5151 ...gment\PYGZus{}id}\PYG{l+s+s2}{\PYGZdq{}}
                                                  
[79] [80] [81] [82] [83] [84] [85] [86] [87] [88]

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l.5758     ├── images
                           

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l.5758     ├── images
                           

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l.5759     ├  ├──animal01.jpg
                                      

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l.5760     ├  ├──animal02.jpg
                                      

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l.5761     ├  ├──animal03.png
                                      

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l.5762     ├── data.csv
                             

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l.5762     ├── data.csv
                             

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l.5762     ├── data.csv
                             
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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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\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 (New in ver-sion v2.24) Met-ric to use for cal-

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\T1/qtm/m/n/10 cu-lat-ing fair-ness. Can be one of \T1/txtt/m/n/10 proportional
Parity\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
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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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\T1/qtm/m/it/10 **record_kwargs\T1/txtt/m/n/10 )|  
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[]\T1/qtm/m/n/10 greater or
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[]\T1/qtm/m/n/10 Op-tional. Fil-ter
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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 par-ti-tion : sub-set of train-ing data to score, one of \T1/t
xtt/m/n/10 datarobot.enums.
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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 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
0 datarobot.TARGET_TYPE.UNSTRUCTURED\T1/qtm/m/n/10 ,

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\T1/qtm/m/n/10 De-ter-mines whether the given cus-tom model is iso-lated, or ca
n ac-cess the

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\T1/qtm/m/n/10 pub-lic net-work. Val-ues: [\T1/qtm/m/it/10 datarobot.NETWORK_EG
RESS_POLICY.NONE\T1/qtm/m/n/10 ,

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T_TYPE.BINARY\T1/qtm/m/n/10 ,

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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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n ac-cess the

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\T1/qtm/m/n/10 pub-lic net-work. Val-ues: [\T1/qtm/m/it/10 datarobot.NETWORK_EG
RESS_POLICY.NONE\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 Ad-di-tional pa-ram-e-ters re-
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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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t value

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tar-get type
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[]\T1/qtm/m/n/10 Ad-di-tional pa-

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[]\T1/qtm/m/n/10 You must
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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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\T1/qtm/m/n/10 - for \T1/qtm/m/it/10 query \T1/qtm/m/n/10 based data-sources - 
`[][]$https : / / s3 . amazonaws . com / datarobot _ test /
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[]\T1/qtm/m/n/10 Sta-tus back-test/holdout train-ing. One of \T1/txtt/m/n/10 da
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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 [bool] Is `'True'' if chal-lenger mod-els is en-abled for this
 de-

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s, 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 pre-dic-tions data col-lec-tion is en-abled for this
 de-ploy-

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t-tings, see
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[]\T1/qtm/m/n/10 [bool] If tar-get pre-dic-tion_warning is en-abled for this de
-ploy-

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\T1/qtm/m/n/10 ment. To cre-ate or up-date ex-ist-ing `'pre-dic-tion_warning'' 
set-tings, see
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[]
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[]\T1/qtm/m/it/10 baseline_validation_job_id\T1/qtm/m/n/10 , 

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

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

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

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

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

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

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

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

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

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

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\T1/qtm/m/it/10 sage=None\T1/txtt/m/n/10 )|  
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[]\T1/qtm/m/n/10 Scores on the
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[]\T1/qtm/m/n/10 {`key':'DataRobot', `sum-mary':{`min':0, `max':29815.0, `std-D
ev':6498.029,
[354] [355] [356] [357]
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[]\T1/qtm/m/n/10 {`key':'DataRobot', `sum-mary':{`min':0, `max':29815.0, `std-D
ev':6498.029,
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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
[361] [362]
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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:
[363]
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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)].

[364]
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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-

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\T1/qtm/m/n/10 ric to get pair-wise data for. Since `v2.19' this is op-tional a
nd 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-f
aults 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
[370]
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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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[393] [394] [395] [396] [397]
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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 ).
[399]
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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
[400] [401] [402]
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[]\T1/qtm/m/n/10 re-sponse

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[]
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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
[405] [406] [407] [408] [409]
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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
[410] [411] [412] [413]
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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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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
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[]\T1/qtm/m/n/10 re-sponse

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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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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
[464] [465]
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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
[466] [467]
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[]\T1/qtm/m/n/10 re-sponse

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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 ).
[471]
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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
[472] [473] [474]
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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
[479]
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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 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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[]
[502]
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[]\T1/qtm/m/n/10 [string] The dataset to make pre-dic-tions against (as up-load
ed from
[503]
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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]
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[]\T1/qtm/m/n/10 re-sponse

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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
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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
[508] [509] [510] [511] [512] [513] [514] [515] [516] [517] [518]
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[]\T1/qtm/m/n/10 [DataSlice, op-tional] Fil-ters the re-turned resid-u-als char
ts by
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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
[530] [531]
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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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[532] [533]
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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
[538] [539] [540]
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[]\T1/qtm/m/n/10 [dict] ^^PPa-ram-e-ters^^Q dic-tio-nary, same as spec-i-fied o
n

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[]

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[541] [542] [543] [544] [545] [546]
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\T1/qtm/m/it/10 low_partial_history_time_series_predictions=False\T1/qtm/m/n/10
 , 
[547]
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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)
[550]
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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 ,
[551]
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[]\T1/qtm/m/n/10 [bool][Switch to us-ing the older a_priori key name in-stead o
f
[552] [553]
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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
[554] [555]
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[]\T1/qtm/m/n/10 (New in ver-sion v2.24)
[556] [557] [558]
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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 ,
[559]
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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.
[560] [561] [562]
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[]\T1/qtm/m/n/10 [pandas.Dataframe] the back-test at-tributes, for-mat-ted into
 a
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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.
[573]
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[]\T1/qtm/m/n/10 Cre-ate pre-dic-tion ex-pla-na-tions for the the dataset used 
to train the model. This can be

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\T1/qtm/m/n/10 re-trieved by call-ing \T1/txtt/m/n/10 dr.Model.get().featurelis
t_id\T1/qtm/m/n/10 . For OTV and time-series projects,

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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.
[574] [575] [576]
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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 )|  
[577]
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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
[587] [588] [589] [590] [591]
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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.
[592]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
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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.
[595] [596]
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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.
[597] [598] [599] [600] [601]
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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
[602] [603] [604] [605] [606] [607] [608] [609] [610] [611] [612]
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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

Underfull \hbox (badness 10000) in paragraph at lines 61028--61038
\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 ).
[613]
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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

Underfull \hbox (badness 8075) in paragraph at lines 61119--61124
[][]\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
[614]
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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 [str] Cho-sen blend method, one from \T1/txtt/m/n/10 datarobot
.enums.
[615]
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[]\T1/qtm/m/n/10 [QUEUE_STATUS enum, op-tional] If called with
[616]
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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
[617]
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[]\T1/qtm/m/n/10 This should be VER-BOSITY_LEVEL.SILENT or VER-
[618] [619]
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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
[620]
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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

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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[621] [622]
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[]\T1/qtm/m/n/10 [PartitioningMethod, op-tional] An in-stance of \T1/txtt/m/n/1
0 datarobot.
[623] [624]
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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.
[625] [626] [627] [628]
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[]\T1/qtm/m/n/10 [str, op-tional] One of datarobot.enums.BiasMitigationTechniqu
e
[629]
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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
[630] [631] [632] [633] [634] [635] [636] [637] [638]
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[]

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

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

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

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

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\T1/qtm/m/it/10 tion_environment=None\T1/txtt/m/n/10 )|  
[642] [643] [644] [645] [646] [647] [648] [649] [650] [651] [652]
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[]\T1/qtm/m/n/10 [float] The to-tal of SHAP val-ues for fea-tures be-yond the
[653] [654] [655] [656] [657] [658] [659] [660]
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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/it/10 not_added_to_menu=None\T1/qtm/m/n/10 , 

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[]\T1/qtm/m/n/10 The list of
[671]
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\T1/qtm/m/it/10 **kwargs\T1/txtt/m/n/10 )|  

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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 The role of 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 , 
[673]
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[]
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[686] [687] [688] [689] [690] [691]
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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 tual_target_value=None\T1/qtm/m/n/10 , 
[692]
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\T1/qtm/m/it/10 ment_page_width\T1/qtm/m/n/10 , 

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

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\T1/qtm/m/it/10 tual_target_value=None\T1/qtm/m/n/10 , 
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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 , 
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[]\T1/qtm/m/n/10 The back-test
[697] [698] [699] [700] [701] [702] [703]
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\T1/qtm/m/it/10 chunk_overlap_percentage\T1/qtm/m/n/10 , 
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[]\T1/qtm/m/it/10 embedding_models\T1/qtm/m/n/10 , 

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\T1/qtm/m/it/10 fault_embedding_model\T1/txtt/m/n/10 )|  

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[]\T1/qtm/m/it/10 text_chunking_configs\T1/txtt/m/n/10 )|  

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

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

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

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

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

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

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

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[708] [709] [710]
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[711] [712]
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[]\T1/qtm/m/n/10 Set-tings for the
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[]\T1/qtm/m/n/10 The new vec-tor

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[]\T1/qtm/m/n/10 Whether to re-move the vec-tor
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[720]
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\T1/qtm/m/it/10 ation_user_id\T1/txtt/m/n/10 )|  
[721] [722] [723]
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\T1/txtt/m/sl/10 create_for_external[][] \T1/qtm/m/n/10 to cre-ate a new reg-is
-tered model ver-sion from an ex-ter-nal model.

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[][]\T1/txtt/m/sl/10 RegisteredModelVersion.list_associated_deployments[][] \T1
/qtm/m/n/10 to list de-ploy-ments as-so-ci-ated with

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\T1/qtm/m/n/10 a reg-is-tered model ver-sion. [][]\T1/txtt/m/sl/10 RegisteredMo
delVersion.create_for_leaderboard_item[][] \T1/qtm/m/n/10 to cre-

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\T1/qtm/m/n/10 ate a new reg-is-tered model ver-sion from a Leader-board model.
 [][]\T1/txtt/m/sl/10 RegisteredModelVersion.

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[]\T1/qtm/m/n/10 Added method meth:\T1/qtm/m/it/10 Deployment.get_predictions_v
s_actuals_over_time <datarobot.models.Deployment.get_predictions_vs_actuals_ove
r_time>
[724]
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[]

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\T1/qtm/m/n/10 All in-ter-ac-tions with \T1/txtt/m/n/10 Project.advanced_option
s \T1/qtm/m/n/10 should be ex-pected to be through the
[725]
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\T1/txtt/m/sl/10 run[][] [][]BatchMonitoringJob.get_status[][] [][]BatchMonitor
ingJob.cancel[][] [][]BatchMonitoringJob.

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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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\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.
[726]
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[]\T1/qtm/m/n/10 If the user con-fig-ures the client via env vars, then set the
 env var

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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>

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\T1/txtt/m/sl/10 request_feature_effect[][] [][]Model.get_or_request_feature_ef
fect[][] [][]Model.get_lift_chart[][]

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[][]\T1/txtt/m/sl/10 Model.get_all_lift_charts[][] [][]Model.get_residuals_char
t[][] [][]Model.get_all_residuals_charts[][]

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[][]\T1/txtt/m/sl/10 Model.request_lift_chart[][] [][]Model.request_residuals_c
hart[][] [][]Model.get_roc_curve[][] [][]Model.
[727]
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[]\T1/qtm/m/n/10 Added new method for re-triev-ing all avail-able fea-ture im-p
acts for the model :meth:

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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.
[728]
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[]\T1/qtm/m/n/10 The sliced in-sight API end-point \T1/qtm/m/it/10 GET: api/v2/
insights/<insight_name>/ \T1/qtm/m/n/10 re-turns a pag-i-nated re-

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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.

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[]\T1/qtm/m/n/10 Dep-re-cated the fol-low-ing meth-ods for re-triev-ing [][]\T1
/txtt/m/sl/10 SharingAccess[][] \T1/qtm/m/n/10 in-for-ma-tion. - [][]\T1/txtt/m
/sl/10 DataStore.
[729]
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[]

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[]

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[730]
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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

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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 ,
[731]
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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.
[732] [733] [734]
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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.

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[][][]\T1/txtt/m/sl/10 CustomModelVersion[][]\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/txtt/m/sl/10 CustomTaskVersion[][]\T1/qtm/m/n/10 's \T1/txtt/m/n/10 r
equired_metadata \T1/qtm/m/n/10 pa-ram-e-ter has been re-moved. Use
[735]
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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.

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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 -
[736]
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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.
[737]
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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
[738] [739]
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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 ,
[740]
Underfull \hbox (badness 6268) in paragraph at lines 75495--75498
[]\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.
[741]
Underfull \hbox (badness 10000) in paragraph at lines 75530--75534
[]\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-

Underfull \hbox (badness 10000) in paragraph at lines 75540--75548
\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-

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\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 75540--75548
[][]\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.
[742]
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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
[743]
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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.
[744] [745]
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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.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.
[746]
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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:

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[]\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.
[747]
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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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[748]
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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.

Underfull \hbox (badness 10000) in paragraph at lines 76405--76411
\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.

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\T1/txtt/m/sl/10 create[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 CustomModelVer
sion.create_clean[][]\T1/qtm/m/n/10 , [][]\T1/txtt/m/sl/10 CustomModelVersion.c
reate_from_previous[][]\T1/qtm/m/n/10 ,
[749]
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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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[750]
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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.
[751]
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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:
[752] [753]
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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.
[754] [755]
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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.
[756]
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[]

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[757]
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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.
[758]
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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.
[759]
Underfull \hbox (badness 10000) in paragraph at lines 77625--77628
[]\T1/qtm/m/n/10 It's now pos-si-ble to get fea-ture as-so-ci-a-tion sta-tuses 
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
[760]
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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
[761] [762] [763]
Underfull \hbox (badness 10000) in paragraph at lines 78042--78051
[]\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.

Underfull \hbox (badness 10000) in paragraph at lines 78081--78083
[][][]\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
[764] [765]
Underfull \hbox (badness 10000) in paragraph at lines 78206--78209
[]\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

Underfull \hbox (badness 10000) in paragraph at lines 78225--78230
[]\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.
[766]
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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.
[767]
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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.
[768]
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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.
[769] [770] [771] [772] [773] [774] [775] [776] [777]
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[]\T1/qtm/m/n/10 its
[778] [779] [780] [781]
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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
[782]
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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
[783] [784] [785] [786] [787] (./datarobot-public-api-client.ind [788]
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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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\T1/qtm/m/it/10 datarobot.models.datetime_trend_plots\T1/qtm/m/n/10 ),

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[]\T1/txtt/m/n/10 AccuracyOverTimePlotPreview \T1/qtm/m/n/10 (\T1/qtm/m/it/10 c
lass in

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\T1/qtm/m/it/10 datarobot.models.datetime_trend_plots\T1/qtm/m/n/10 ),

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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/qtm/m/it/10 datarobot.models.datetime_trend_plots\T1/qtm/m/n/10 ),

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

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

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[]\T1/txtt/m/n/10 AdvancedTuningParamsType \T1/qtm/m/n/10 (\T1/qtm/m/it/10 clas
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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[]\T1/txtt/m/n/10 analyze_and_model() \T1/qtm/m/n/10 (\T1/qtm/m/it/10 datarobot
.models.Project

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[]\T1/txtt/m/n/10 AnomalyAssessmentDataPoint \T1/qtm/m/n/10 (\T1/qtm/m/it/10 cl
ass in

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

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\T1/qtm/m/it/10 datarobot.models.anomaly_assessment\T1/qtm/m/n/10 ),

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

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[]\T1/txtt/m/n/10 AnomalyAssessmentPreviewBin \T1/qtm/m/n/10 (\T1/qtm/m/it/10 c
lass in

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

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\T1/qtm/m/it/10 datarobot.models.anomaly_assessment\T1/qtm/m/n/10 ),

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

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

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\T1/qtm/m/it/10 datarobot.models.datetime_trend_plots\T1/qtm/m/n/10 ),

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[]\T1/txtt/m/n/10 AnomalyOverTimePlotPreview \T1/qtm/m/n/10 (\T1/qtm/m/it/10 cl
ass in

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\T1/qtm/m/it/10 datarobot.models.datetime_trend_plots\T1/qtm/m/n/10 ),

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class in

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api_object.APIObject

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BlenderModel class

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DatetimeModel class

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Featurelist class

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PrimeModel class

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RatingTableModel

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CustomTask class

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CustomTaskVersion

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models.Blueprint

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models.Deployment

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models.Featurelist

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models.PayoffMatrix

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ngFeature class

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ngFeaturelist class

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seConditionalProbabilities

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tionDataset class

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TableModel class

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eredModel class

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onshipsConfiguration

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