.. include:: _contributors.rst

.. currentmodule:: sklearn

.. _release_notes_0_22:

============
Version 0.22
============

For a short description of the main highlights of the release, please refer to
:ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_22_0.py`.

.. include:: changelog_legend.inc

.. _changes_0_22_2:

Version 0.22.2.post1
====================

**March 3 2020**

The 0.22.2.post1 release includes a packaging fix for the source distribution
but the content of the packages is otherwise identical to the content of the
wheels with the 0.22.2 version (without the .post1 suffix). Both contain the
following changes.

Changelog
---------

:mod:`sklearn.impute`
.....................

- |Efficiency| Reduce :func:`impute.KNNImputer` asymptotic memory usage by
  chunking pairwise distance computation.
  :pr:`16397` by `Joel Nothman`_.

:mod:`sklearn.metrics`
......................

- |Fix| Fixed a bug in `metrics.plot_roc_curve` where
  the name of the estimator was passed in the :class:`metrics.RocCurveDisplay`
  instead of the parameter `name`. It results in a different plot when calling
  :meth:`metrics.RocCurveDisplay.plot` for the subsequent times.
  :pr:`16500` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| Fixed a bug in `metrics.plot_precision_recall_curve` where the
  name of the estimator was passed in the
  :class:`metrics.PrecisionRecallDisplay` instead of the parameter `name`. It
  results in a different plot when calling
  :meth:`metrics.PrecisionRecallDisplay.plot` for the subsequent times.
  :pr:`16505` by :user:`Guillaume Lemaitre <glemaitre>`.

:mod:`sklearn.neighbors`
........................

- |Fix| Fix a bug which converted a list of arrays into a 2-D object
  array instead of a 1-D array containing NumPy arrays. This bug
  was affecting :meth:`neighbors.NearestNeighbors.radius_neighbors`.
  :pr:`16076` by :user:`Guillaume Lemaitre <glemaitre>` and
  :user:`Alex Shacked <alexshacked>`.

.. _changes_0_22_1:

Version 0.22.1
==============

**January 2 2020**

This is a bug-fix release to primarily resolve some packaging issues in version
0.22.0. It also includes minor documentation improvements and some bug fixes.

Changelog
---------


:mod:`sklearn.cluster`
......................

- |Fix| :class:`cluster.KMeans` with ``algorithm="elkan"`` now uses the same
  stopping criterion as with the default ``algorithm="full"``. :pr:`15930` by
  :user:`inder128`.

:mod:`sklearn.inspection`
.........................

- |Fix| :func:`inspection.permutation_importance` will return the same
  `importances` when a `random_state` is given for both `n_jobs=1` or
  `n_jobs>1` both with shared memory backends (thread-safety) and
  isolated memory, process-based backends.
  Also avoid casting the data as object dtype and avoid read-only error
  on large dataframes with `n_jobs>1` as reported in :issue:`15810`.
  Follow-up of :pr:`15898` by :user:`Shivam Gargsya <shivamgargsya>`.
  :pr:`15933` by :user:`Guillaume Lemaitre <glemaitre>` and `Olivier Grisel`_.

- |Fix| `inspection.plot_partial_dependence` and
  :meth:`inspection.PartialDependenceDisplay.plot` now consistently checks
  the number of axes passed in. :pr:`15760` by `Thomas Fan`_.

:mod:`sklearn.metrics`
......................

- |Fix| `metrics.plot_confusion_matrix` now raises error when `normalize`
  is invalid. Previously, it runs fine with no normalization.
  :pr:`15888` by `Hanmin Qin`_.

- |Fix| `metrics.plot_confusion_matrix` now colors the label color
  correctly to maximize contrast with its background. :pr:`15936` by
  `Thomas Fan`_ and :user:`DizietAsahi`.

- |Fix| :func:`metrics.classification_report` does no longer ignore the
  value of the ``zero_division`` keyword argument. :pr:`15879`
  by :user:`Bibhash Chandra Mitra <Bibyutatsu>`.

- |Fix| Fixed a bug in `metrics.plot_confusion_matrix` to correctly
  pass the `values_format` parameter to the :class:`metrics.ConfusionMatrixDisplay`
  plot() call. :pr:`15937` by :user:`Stephen Blystone <blynotes>`.

:mod:`sklearn.model_selection`
..............................

- |Fix| :class:`model_selection.GridSearchCV` and
  :class:`model_selection.RandomizedSearchCV` accept scalar values provided in
  `fit_params`. Change in 0.22 was breaking backward compatibility.
  :pr:`15863` by :user:`Adrin Jalali <adrinjalali>` and
  :user:`Guillaume Lemaitre <glemaitre>`.

:mod:`sklearn.naive_bayes`
..........................

- |Fix| Removed `abstractmethod` decorator for the method `_check_X` in
  `naive_bayes.BaseNB` that could break downstream projects inheriting
  from this deprecated public base class. :pr:`15996` by
  :user:`Brigitta Sipőcz <bsipocz>`.

:mod:`sklearn.preprocessing`
............................

- |Fix| :class:`preprocessing.QuantileTransformer` now guarantees the
  `quantiles_` attribute to be completely sorted in non-decreasing manner.
  :pr:`15751` by :user:`Tirth Patel <tirthasheshpatel>`.

:mod:`sklearn.semi_supervised`
..............................

- |Fix| :class:`semi_supervised.LabelPropagation` and
  :class:`semi_supervised.LabelSpreading` now allow callable kernel function to
  return sparse weight matrix.
  :pr:`15868` by :user:`Niklas Smedemark-Margulies <nik-sm>`.

:mod:`sklearn.utils`
....................

- |Fix| :func:`utils.check_array` now correctly converts pandas DataFrame with
  boolean columns to floats. :pr:`15797` by `Thomas Fan`_.

- |Fix| :func:`utils.validation.check_is_fitted` accepts back an explicit ``attributes``
  argument to check for specific attributes as explicit markers of a fitted
  estimator. When no explicit ``attributes`` are provided, only the attributes
  that end with a underscore and do not start with double underscore are used
  as "fitted" markers. The ``all_or_any`` argument is also no longer
  deprecated. This change is made to restore some backward compatibility with
  the behavior of this utility in version 0.21. :pr:`15947` by `Thomas Fan`_.

.. _changes_0_22:

Version 0.22.0
==============

**December 3 2019**

Website update
--------------

`Our website <https://scikit-learn.org/>`_ was revamped and given a fresh
new look. :pr:`14849` by `Thomas Fan`_.

Clear definition of the public API
----------------------------------

Scikit-learn has a public API, and a private API.

We do our best not to break the public API, and to only introduce
backward-compatible changes that do not require any user action. However, in
cases where that's not possible, any change to the public API is subject to
a deprecation cycle of two minor versions. The private API isn't publicly
documented and isn't subject to any deprecation cycle, so users should not
rely on its stability.

A function or object is public if it is documented in the `API Reference
<https://scikit-learn.org/dev/modules/classes.html>`_ and if it can be
imported with an import path without leading underscores. For example
``sklearn.pipeline.make_pipeline`` is public, while
`sklearn.pipeline._name_estimators` is private.
``sklearn.ensemble._gb.BaseEnsemble`` is private too because the whole `_gb`
module is private.

Up to 0.22, some tools were de-facto public (no leading underscore), while
they should have been private in the first place. In version 0.22, these
tools have been made properly private, and the public API space has been
cleaned. In addition, importing from most sub-modules is now deprecated: you
should for example use ``from sklearn.cluster import Birch`` instead of
``from sklearn.cluster.birch import Birch`` (in practice, ``birch.py`` has
been moved to ``_birch.py``).

.. note::

    All the tools in the public API should be documented in the `API
    Reference <https://scikit-learn.org/dev/modules/classes.html>`_. If you
    find a public tool (without leading underscore) that isn't in the API
    reference, that means it should either be private or documented. Please
    let us know by opening an issue!

This work was tracked in `issue 9250
<https://github.com/scikit-learn/scikit-learn/issues/9250>`_ and `issue
12927 <https://github.com/scikit-learn/scikit-learn/issues/12927>`_.


Deprecations: using ``FutureWarning`` from now on
-------------------------------------------------

When deprecating a feature, previous versions of scikit-learn used to raise
a ``DeprecationWarning``. Since the ``DeprecationWarnings`` aren't shown by
default by Python, scikit-learn needed to resort to a custom warning filter
to always show the warnings. That filter would sometimes interfere
with users custom warning filters.

Starting from version 0.22, scikit-learn will show ``FutureWarnings`` for
deprecations, `as recommended by the Python documentation
<https://docs.python.org/3/library/exceptions.html#FutureWarning>`_.
``FutureWarnings`` are always shown by default by Python, so the custom
filter has been removed and scikit-learn no longer hinders with user
filters. :pr:`15080` by `Nicolas Hug`_.

Changed models
--------------

The following estimators and functions, when fit with the same data and
parameters, may produce different models from the previous version. This often
occurs due to changes in the modelling logic (bug fixes or enhancements), or in
random sampling procedures.

- :class:`cluster.KMeans` when `n_jobs=1`. |Fix|
- :class:`decomposition.SparseCoder`,
  :class:`decomposition.DictionaryLearning`, and
  :class:`decomposition.MiniBatchDictionaryLearning` |Fix|
- :class:`decomposition.SparseCoder` with `algorithm='lasso_lars'` |Fix|
- :class:`decomposition.SparsePCA` where `normalize_components` has no effect
  due to deprecation.
- :class:`ensemble.HistGradientBoostingClassifier` and
  :class:`ensemble.HistGradientBoostingRegressor` |Fix|, |Feature|,
  |Enhancement|.
- :class:`impute.IterativeImputer` when `X` has features with no missing
  values. |Feature|
- :class:`linear_model.Ridge` when `X` is sparse. |Fix|
- :class:`model_selection.StratifiedKFold` and any use of `cv=int` with a
  classifier. |Fix|
- :class:`cross_decomposition.CCA` when using scipy >= 1.3 |Fix|

Details are listed in the changelog below.

(While we are trying to better inform users by providing this information, we
cannot assure that this list is complete.)

Changelog
---------

..
    Entries should be grouped by module (in alphabetic order) and prefixed with
    one of the labels: |MajorFeature|, |Feature|, |Efficiency|, |Enhancement|,
    |Fix| or |API| (see whats_new.rst for descriptions).
    Entries should be ordered by those labels (e.g. |Fix| after |Efficiency|).
    Changes not specific to a module should be listed under *Multiple Modules*
    or *Miscellaneous*.
    Entries should end with:
    :pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
    where 123456 is the *pull request* number, not the issue number.

:mod:`sklearn.base`
...................

- |API| From version 0.24 :meth:`base.BaseEstimator.get_params` will raise an
  AttributeError rather than return None for parameters that are in the
  estimator's constructor but not stored as attributes on the instance.
  :pr:`14464` by `Joel Nothman`_.

:mod:`sklearn.calibration`
..........................

- |Fix| Fixed a bug that made :class:`calibration.CalibratedClassifierCV` fail when
  given a `sample_weight` parameter of type `list` (in the case where
  `sample_weights` are not supported by the wrapped estimator). :pr:`13575`
  by :user:`William de Vazelhes <wdevazelhes>`.

:mod:`sklearn.cluster`
......................

- |Feature| :class:`cluster.SpectralClustering` now accepts precomputed sparse
  neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`_ and
  :user:`Kumar Ashutosh <thechargedneutron>`.

- |Enhancement| :class:`cluster.SpectralClustering` now accepts a ``n_components``
  parameter. This parameter extends `SpectralClustering` class functionality to
  match :meth:`cluster.spectral_clustering`.
  :pr:`13726` by :user:`Shuzhe Xiao <fdas3213>`.

- |Fix| Fixed a bug where :class:`cluster.KMeans` produced inconsistent results
  between `n_jobs=1` and `n_jobs>1` due to the handling of the random state.
  :pr:`9288` by :user:`Bryan Yang <bryanyang0528>`.

- |Fix| Fixed a bug where `elkan` algorithm in :class:`cluster.KMeans` was
  producing Segmentation Fault on large arrays due to integer index overflow.
  :pr:`15057` by :user:`Vladimir Korolev <balodja>`.

- |Fix| :class:`~cluster.MeanShift` now accepts a :term:`max_iter` with a
  default value of 300 instead of always using the default 300. It also now
  exposes an ``n_iter_`` indicating the maximum number of iterations performed
  on each seed. :pr:`15120` by `Adrin Jalali`_.

- |Fix| :class:`cluster.AgglomerativeClustering` and
  :class:`cluster.FeatureAgglomeration` now raise an error if
  `affinity='cosine'` and `X` has samples that are all-zeros. :pr:`7943` by
  :user:`mthorrell`.

:mod:`sklearn.compose`
......................

- |Feature|  Adds :func:`compose.make_column_selector` which is used with
  :class:`compose.ColumnTransformer` to select DataFrame columns on the basis
  of name and dtype. :pr:`12303` by `Thomas Fan`_.

- |Fix| Fixed a bug in :class:`compose.ColumnTransformer` which failed to
  select the proper columns when using a boolean list, with NumPy older than
  1.12.
  :pr:`14510` by `Guillaume Lemaitre`_.

- |Fix| Fixed a bug in :class:`compose.TransformedTargetRegressor` which did not
  pass `**fit_params` to the underlying regressor.
  :pr:`14890` by :user:`Miguel Cabrera <mfcabrera>`.

- |Fix| The :class:`compose.ColumnTransformer` now requires the number of
  features to be consistent between `fit` and `transform`. A `FutureWarning`
  is raised now, and this will raise an error in 0.24. If the number of
  features isn't consistent and negative indexing is used, an error is
  raised. :pr:`14544` by `Adrin Jalali`_.

:mod:`sklearn.cross_decomposition`
..................................

- |Feature| :class:`cross_decomposition.PLSCanonical` and
  :class:`cross_decomposition.PLSRegression` have a new function
  ``inverse_transform`` to transform data to the original space.
  :pr:`15304` by :user:`Jaime Ferrando Huertas <jiwidi>`.

- |Enhancement| :class:`decomposition.KernelPCA` now properly checks the
  eigenvalues found by the solver for numerical or conditioning issues. This
  ensures consistency of results across solvers (different choices for
  ``eigen_solver``), including approximate solvers such as ``'randomized'`` and
  ``'lobpcg'`` (see :issue:`12068`).
  :pr:`12145` by :user:`Sylvain Marié <smarie>`

- |Fix| Fixed a bug where :class:`cross_decomposition.PLSCanonical` and
  :class:`cross_decomposition.PLSRegression` were raising an error when fitted
  with a target matrix `Y` in which the first column was constant.
  :issue:`13609` by :user:`Camila Williamson <camilaagw>`.

- |Fix| :class:`cross_decomposition.CCA` now produces the same results with
  scipy 1.3 and previous scipy versions. :pr:`15661` by `Thomas Fan`_.

:mod:`sklearn.datasets`
.......................

- |Feature| :func:`datasets.fetch_openml` now supports heterogeneous data using
  pandas by setting `as_frame=True`. :pr:`13902` by `Thomas Fan`_.

- |Feature| :func:`datasets.fetch_openml` now includes the `target_names` in
  the returned Bunch. :pr:`15160` by `Thomas Fan`_.

- |Enhancement| The parameter `return_X_y` was added to
  :func:`datasets.fetch_20newsgroups` and :func:`datasets.fetch_olivetti_faces`
  . :pr:`14259` by :user:`Sourav Singh <souravsingh>`.

- |Enhancement| :func:`datasets.make_classification` now accepts array-like
  `weights` parameter, i.e. list or numpy.array, instead of list only.
  :pr:`14764` by :user:`Cat Chenal <CatChenal>`.

- |Enhancement| The parameter `normalize` was added to
   :func:`datasets.fetch_20newsgroups_vectorized`.
   :pr:`14740` by :user:`Stéphan Tulkens <stephantul>`

- |Fix| Fixed a bug in :func:`datasets.fetch_openml`, which failed to load
  an OpenML dataset that contains an ignored feature.
  :pr:`14623` by :user:`Sarra Habchi <HabchiSarra>`.

:mod:`sklearn.decomposition`
............................

- |Efficiency| :class:`decomposition.NMF` with `solver="mu"` fitted on sparse input
  matrices now uses batching to avoid briefly allocating an array with size
  (#non-zero elements, n_components). :pr:`15257` by :user:`Mart Willocx <Maocx>`.

- |Enhancement| :func:`decomposition.dict_learning` and
  :func:`decomposition.dict_learning_online` now accept `method_max_iter` and
  pass it to :meth:`decomposition.sparse_encode`.
  :issue:`12650` by `Adrin Jalali`_.

- |Enhancement| :class:`decomposition.SparseCoder`,
  :class:`decomposition.DictionaryLearning`, and
  :class:`decomposition.MiniBatchDictionaryLearning` now take a
  `transform_max_iter` parameter and pass it to either
  :func:`decomposition.dict_learning()` or
  :func:`decomposition.sparse_encode()`. :issue:`12650` by `Adrin Jalali`_.

- |Enhancement| :class:`decomposition.IncrementalPCA` now accepts sparse
  matrices as input, converting them to dense in batches thereby avoiding the
  need to store the entire dense matrix at once.
  :pr:`13960` by :user:`Scott Gigante <scottgigante>`.

- |Fix| :func:`decomposition.sparse_encode()` now passes the `max_iter` to the
  underlying :class:`linear_model.LassoLars` when `algorithm='lasso_lars'`.
  :issue:`12650` by `Adrin Jalali`_.

:mod:`sklearn.dummy`
....................

- |Fix| :class:`dummy.DummyClassifier` now handles checking the existence
  of the provided constant in multiouput cases.
  :pr:`14908` by :user:`Martina G. Vilas <martinagvilas>`.

- |API| The default value of the `strategy` parameter in
  :class:`dummy.DummyClassifier` will change from `'stratified'` in version
  0.22 to `'prior'` in 0.24. A FutureWarning is raised when the default value
  is used. :pr:`15382` by `Thomas Fan`_.

- |API| The ``outputs_2d_`` attribute is deprecated in
  :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor`. It is
  equivalent to ``n_outputs > 1``. :pr:`14933` by `Nicolas Hug`_

:mod:`sklearn.ensemble`
.......................

- |MajorFeature| Added :class:`ensemble.StackingClassifier` and
  :class:`ensemble.StackingRegressor` to stack predictors using a final
  classifier or regressor.  :pr:`11047` by :user:`Guillaume Lemaitre
  <glemaitre>` and :user:`Caio Oliveira <caioaao>` and :pr:`15138` by
  :user:`Jon Cusick <jcusick13>`..

- |MajorFeature| Many improvements were made to
  :class:`ensemble.HistGradientBoostingClassifier` and
  :class:`ensemble.HistGradientBoostingRegressor`:

  - |Feature| Estimators now natively support dense data with missing
    values both for training and predicting. They also support infinite
    values. :pr:`13911` and :pr:`14406` by `Nicolas Hug`_, `Adrin Jalali`_
    and `Olivier Grisel`_.
  - |Feature| Estimators now have an additional `warm_start` parameter that
    enables warm starting. :pr:`14012` by :user:`Johann Faouzi <johannfaouzi>`.
  - |Feature| :func:`inspection.partial_dependence` and
    `inspection.plot_partial_dependence` now support the fast 'recursion'
    method for both estimators. :pr:`13769` by `Nicolas Hug`_.
  - |Enhancement| for :class:`ensemble.HistGradientBoostingClassifier` the
    training loss or score is now monitored on a class-wise stratified
    subsample to preserve the class balance of the original training set.
    :pr:`14194` by :user:`Johann Faouzi <johannfaouzi>`.
  - |Enhancement| :class:`ensemble.HistGradientBoostingRegressor` now supports
    the 'least_absolute_deviation' loss. :pr:`13896` by `Nicolas Hug`_.
  - |Fix| Estimators now bin the training and validation data separately to
    avoid any data leak. :pr:`13933` by `Nicolas Hug`_.
  - |Fix| Fixed a bug where early stopping would break with string targets.
    :pr:`14710` by `Guillaume Lemaitre`_.
  - |Fix| :class:`ensemble.HistGradientBoostingClassifier` now raises an error
    if ``categorical_crossentropy`` loss is given for a binary classification
    problem. :pr:`14869` by `Adrin Jalali`_.

  Note that pickles from 0.21 will not work in 0.22.

- |Enhancement| Addition of ``max_samples`` argument allows limiting
  size of bootstrap samples to be less than size of dataset. Added to
  :class:`ensemble.RandomForestClassifier`,
  :class:`ensemble.RandomForestRegressor`,
  :class:`ensemble.ExtraTreesClassifier`,
  :class:`ensemble.ExtraTreesRegressor`. :pr:`14682` by
  :user:`Matt Hancock <notmatthancock>` and
  :pr:`5963` by :user:`Pablo Duboue <DrDub>`.

- |Fix| :func:`ensemble.VotingClassifier.predict_proba` will no longer be
  present when `voting='hard'`. :pr:`14287` by `Thomas Fan`_.

- |Fix| The `named_estimators_` attribute in :class:`ensemble.VotingClassifier`
  and :class:`ensemble.VotingRegressor` now correctly maps to dropped estimators.
  Previously, the `named_estimators_` mapping was incorrect whenever one of the
  estimators was dropped. :pr:`15375` by `Thomas Fan`_.

- |Fix| Run by default
  :func:`utils.estimator_checks.check_estimator` on both
  :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor`. It
  leads to solve issues regarding shape consistency during `predict` which was
  failing when the underlying estimators were not outputting consistent array
  dimensions. Note that it should be replaced by refactoring the common tests
  in the future.
  :pr:`14305` by `Guillaume Lemaitre`_.

- |Fix| :class:`ensemble.AdaBoostClassifier` computes probabilities based on
  the decision function as in the literature. Thus, `predict` and
  `predict_proba` give consistent results.
  :pr:`14114` by `Guillaume Lemaitre`_.

- |Fix| Stacking and Voting estimators now ensure that their underlying
  estimators are either all classifiers or all regressors.
  :class:`ensemble.StackingClassifier`, :class:`ensemble.StackingRegressor`,
  and :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor`
  now raise consistent error messages.
  :pr:`15084` by `Guillaume Lemaitre`_.

- |Fix| :class:`ensemble.AdaBoostRegressor` where the loss should be normalized
  by the max of the samples with non-null weights only.
  :pr:`14294` by `Guillaume Lemaitre`_.

- |API| ``presort`` is now deprecated in
  :class:`ensemble.GradientBoostingClassifier` and
  :class:`ensemble.GradientBoostingRegressor`, and the parameter has no effect.
  Users are recommended to use :class:`ensemble.HistGradientBoostingClassifier`
  and :class:`ensemble.HistGradientBoostingRegressor` instead.
  :pr:`14907` by `Adrin Jalali`_.

:mod:`sklearn.feature_extraction`
.................................

- |Enhancement| A warning  will  now be raised  if a parameter choice means
  that another parameter will be unused on calling the fit() method for
  :class:`feature_extraction.text.HashingVectorizer`,
  :class:`feature_extraction.text.CountVectorizer` and
  :class:`feature_extraction.text.TfidfVectorizer`.
  :pr:`14602` by :user:`Gaurav Chawla <getgaurav2>`.

- |Fix| Functions created by ``build_preprocessor`` and ``build_analyzer`` of
  `feature_extraction.text.VectorizerMixin` can now be pickled.
  :pr:`14430` by :user:`Dillon Niederhut <deniederhut>`.

- |Fix| `feature_extraction.text.strip_accents_unicode` now correctly
  removes accents from strings that are in NFKD normalized form. :pr:`15100` by
  :user:`Daniel Grady <DGrady>`.

- |Fix| Fixed a bug that caused :class:`feature_extraction.DictVectorizer` to raise
  an `OverflowError` during the `transform` operation when producing a `scipy.sparse`
  matrix on large input data. :pr:`15463` by :user:`Norvan Sahiner <norvan>`.

- |API| Deprecated unused `copy` param for
  :meth:`feature_extraction.text.TfidfVectorizer.transform` it will be
  removed in v0.24. :pr:`14520` by
  :user:`Guillem G. Subies <guillemgsubies>`.

:mod:`sklearn.feature_selection`
................................

- |Enhancement| Updated the following :mod:`sklearn.feature_selection`
  estimators to allow NaN/Inf values in ``transform`` and ``fit``:
  :class:`feature_selection.RFE`, :class:`feature_selection.RFECV`,
  :class:`feature_selection.SelectFromModel`,
  and :class:`feature_selection.VarianceThreshold`. Note that if the underlying
  estimator of the feature selector does not allow NaN/Inf then it will still
  error, but the feature selectors themselves no longer enforce this
  restriction unnecessarily. :issue:`11635` by :user:`Alec Peters <adpeters>`.

- |Fix| Fixed a bug where :class:`feature_selection.VarianceThreshold` with
  `threshold=0` did not remove constant features due to numerical instability,
  by using range rather than variance in this case.
  :pr:`13704` by :user:`Roddy MacSween <rlms>`.

:mod:`sklearn.gaussian_process`
...............................

- |Feature| Gaussian process models on structured data: :class:`gaussian_process.GaussianProcessRegressor`
  and :class:`gaussian_process.GaussianProcessClassifier` can now accept a list
  of generic objects (e.g. strings, trees, graphs, etc.) as the ``X`` argument
  to their training/prediction methods.
  A user-defined kernel should be provided for computing the kernel matrix among
  the generic objects, and should inherit from `gaussian_process.kernels.GenericKernelMixin`
  to notify the GPR/GPC model that it handles non-vectorial samples.
  :pr:`15557` by :user:`Yu-Hang Tang <yhtang>`.

- |Efficiency| :func:`gaussian_process.GaussianProcessClassifier.log_marginal_likelihood`
  and :func:`gaussian_process.GaussianProcessRegressor.log_marginal_likelihood` now
  accept a ``clone_kernel=True`` keyword argument. When set to ``False``,
  the kernel attribute is modified, but may result in a performance improvement.
  :pr:`14378` by :user:`Masashi Shibata <c-bata>`.

- |API| From version 0.24 :meth:`gaussian_process.kernels.Kernel.get_params` will raise an
  ``AttributeError`` rather than return ``None`` for parameters that are in the
  estimator's constructor but not stored as attributes on the instance.
  :pr:`14464` by `Joel Nothman`_.

:mod:`sklearn.impute`
.....................

- |MajorFeature| Added :class:`impute.KNNImputer`, to impute missing values using
  k-Nearest Neighbors. :issue:`12852` by :user:`Ashim Bhattarai <ashimb9>` and
  `Thomas Fan`_ and :pr:`15010` by `Guillaume Lemaitre`_.

- |Feature| :class:`impute.IterativeImputer` has new `skip_compute` flag that
  is False by default, which, when True, will skip computation on features that
  have no missing values during the fit phase. :issue:`13773` by
  :user:`Sergey Feldman <sergeyf>`.

- |Efficiency| :meth:`impute.MissingIndicator.fit_transform` avoid repeated
  computation of the masked matrix. :pr:`14356` by :user:`Harsh Soni <harsh020>`.

- |Fix| :class:`impute.IterativeImputer` now works when there is only one feature.
  By :user:`Sergey Feldman <sergeyf>`.

- |Fix| Fixed a bug in :class:`impute.IterativeImputer` where features where
  imputed in the reverse desired order with ``imputation_order`` either
  ``"ascending"`` or ``"descending"``. :pr:`15393` by
  :user:`Venkatachalam N <venkyyuvy>`.

:mod:`sklearn.inspection`
.........................

- |MajorFeature| :func:`inspection.permutation_importance` has been added to
  measure the importance of each feature in an arbitrary trained model with
  respect to a given scoring function. :issue:`13146` by `Thomas Fan`_.

- |Feature| :func:`inspection.partial_dependence` and
  `inspection.plot_partial_dependence` now support the fast 'recursion'
  method for :class:`ensemble.HistGradientBoostingClassifier` and
  :class:`ensemble.HistGradientBoostingRegressor`. :pr:`13769` by
  `Nicolas Hug`_.

- |Enhancement| `inspection.plot_partial_dependence` has been extended to
  now support the new visualization API described in the :ref:`User Guide
  <visualizations>`. :pr:`14646` by `Thomas Fan`_.

- |Enhancement| :func:`inspection.partial_dependence` accepts pandas DataFrame
  and :class:`pipeline.Pipeline` containing :class:`compose.ColumnTransformer`.
  In addition `inspection.plot_partial_dependence` will use the column
  names by default when a dataframe is passed.
  :pr:`14028` and :pr:`15429` by `Guillaume Lemaitre`_.

:mod:`sklearn.kernel_approximation`
...................................

- |Fix| Fixed a bug where :class:`kernel_approximation.Nystroem` raised a
  `KeyError` when using `kernel="precomputed"`.
  :pr:`14706` by :user:`Venkatachalam N <venkyyuvy>`.

:mod:`sklearn.linear_model`
...........................

- |Efficiency| The 'liblinear' logistic regression solver is now faster and
  requires less memory.
  :pr:`14108`, :pr:`14170`, :pr:`14296` by :user:`Alex Henrie <alexhenrie>`.

- |Enhancement| :class:`linear_model.BayesianRidge` now accepts hyperparameters
  ``alpha_init`` and ``lambda_init`` which can be used to set the initial value
  of the maximization procedure in :term:`fit`.
  :pr:`13618` by :user:`Yoshihiro Uchida <c56pony>`.

- |Fix| :class:`linear_model.Ridge` now correctly fits an intercept when `X` is
  sparse, `solver="auto"` and `fit_intercept=True`, because the default solver
  in this configuration has changed to `sparse_cg`, which can fit an intercept
  with sparse data. :pr:`13995` by :user:`Jérôme Dockès <jeromedockes>`.

- |Fix| :class:`linear_model.Ridge` with `solver='sag'` now accepts F-ordered
  and non-contiguous arrays and makes a conversion instead of failing.
  :pr:`14458` by `Guillaume Lemaitre`_.

- |Fix| :class:`linear_model.LassoCV` no longer forces ``precompute=False``
  when fitting the final model. :pr:`14591` by `Andreas Müller`_.

- |Fix| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV`
  now correctly scores when `cv=None`.
  :pr:`14864` by :user:`Venkatachalam N <venkyyuvy>`.

- |Fix| Fixed a bug in :class:`linear_model.LogisticRegressionCV` where the
  ``scores_``, ``n_iter_`` and ``coefs_paths_`` attribute would have a wrong
  ordering with ``penalty='elastic-net'``. :pr:`15044` by `Nicolas Hug`_

- |Fix| :class:`linear_model.MultiTaskLassoCV` and
  :class:`linear_model.MultiTaskElasticNetCV` with X of dtype int
  and `fit_intercept=True`.
  :pr:`15086` by :user:`Alex Gramfort <agramfort>`.

- |Fix| The liblinear solver now supports ``sample_weight``.
  :pr:`15038` by `Guillaume Lemaitre`_.

:mod:`sklearn.manifold`
.......................

- |Feature| :class:`manifold.Isomap`, :class:`manifold.TSNE`, and
  :class:`manifold.SpectralEmbedding` now accept precomputed sparse
  neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`_ and
  :user:`Kumar Ashutosh <thechargedneutron>`.

- |Feature| Exposed the ``n_jobs`` parameter in :class:`manifold.TSNE` for
  multi-core calculation of the neighbors graph. This parameter has no
  impact when ``metric="precomputed"`` or (``metric="euclidean"`` and
  ``method="exact"``). :issue:`15082` by `Roman Yurchak`_.

- |Efficiency| Improved efficiency of :class:`manifold.TSNE` when
  ``method="barnes-hut"`` by computing the gradient in parallel.
  :pr:`13213` by :user:`Thomas Moreau <tommoral>`

- |Fix| Fixed a bug where :func:`manifold.spectral_embedding` (and therefore
  :class:`manifold.SpectralEmbedding` and :class:`cluster.SpectralClustering`)
  computed wrong eigenvalues with ``eigen_solver='amg'`` when
  ``n_samples < 5 * n_components``. :pr:`14647` by `Andreas Müller`_.

- |Fix| Fixed a bug in :func:`manifold.spectral_embedding`  used in
  :class:`manifold.SpectralEmbedding` and :class:`cluster.SpectralClustering`
  where ``eigen_solver="amg"`` would sometimes result in a LinAlgError.
  :issue:`13393` by :user:`Andrew Knyazev <lobpcg>`
  :pr:`13707` by :user:`Scott White <whitews>`

- |API| Deprecate ``training_data_`` unused attribute in
  :class:`manifold.Isomap`. :issue:`10482` by `Tom Dupre la Tour`_.

:mod:`sklearn.metrics`
......................

- |MajorFeature| `metrics.plot_roc_curve` has been added to plot roc
  curves. This function introduces the visualization API described in
  the :ref:`User Guide <visualizations>`. :pr:`14357` by `Thomas Fan`_.

- |Feature| Added a new parameter ``zero_division`` to multiple classification
  metrics: :func:`metrics.precision_score`, :func:`metrics.recall_score`,
  :func:`metrics.f1_score`, :func:`metrics.fbeta_score`,
  :func:`metrics.precision_recall_fscore_support`,
  :func:`metrics.classification_report`. This allows to set returned value for
  ill-defined metrics.
  :pr:`14900` by :user:`Marc Torrellas Socastro <marctorrellas>`.

- |Feature| Added the :func:`metrics.pairwise.nan_euclidean_distances` metric,
  which calculates euclidean distances in the presence of missing values.
  :issue:`12852` by :user:`Ashim Bhattarai <ashimb9>` and `Thomas Fan`_.

- |Feature| New ranking metrics :func:`metrics.ndcg_score` and
  :func:`metrics.dcg_score` have been added to compute Discounted Cumulative
  Gain and Normalized Discounted Cumulative Gain. :pr:`9951` by :user:`Jérôme
  Dockès <jeromedockes>`.

- |Feature| `metrics.plot_precision_recall_curve` has been added to plot
  precision recall curves. :pr:`14936` by `Thomas Fan`_.

- |Feature| `metrics.plot_confusion_matrix` has been added to plot
  confusion matrices. :pr:`15083` by `Thomas Fan`_.

- |Feature| Added multiclass support to :func:`metrics.roc_auc_score` with
  corresponding scorers `'roc_auc_ovr'`, `'roc_auc_ovo'`,
  `'roc_auc_ovr_weighted'`, and `'roc_auc_ovo_weighted'`.
  :pr:`12789` and :pr:`15274` by
  :user:`Kathy Chen <kathyxchen>`, :user:`Mohamed Maskani <maskani-moh>`, and
  `Thomas Fan`_.

- |Feature| Add :class:`metrics.mean_tweedie_deviance` measuring the
  Tweedie deviance for a given ``power`` parameter. Also add mean Poisson
  deviance :class:`metrics.mean_poisson_deviance` and mean Gamma deviance
  :class:`metrics.mean_gamma_deviance` that are special cases of the Tweedie
  deviance for ``power=1`` and ``power=2`` respectively.
  :pr:`13938` by :user:`Christian Lorentzen <lorentzenchr>` and
  `Roman Yurchak`_.

- |Efficiency| Improved performance of
  :func:`metrics.pairwise.manhattan_distances` in the case of sparse matrices.
  :pr:`15049` by `Paolo Toccaceli <ptocca>`.

- |Enhancement| The parameter ``beta`` in :func:`metrics.fbeta_score` is
  updated to accept the zero and `float('+inf')` value.
  :pr:`13231` by :user:`Dong-hee Na <corona10>`.

- |Enhancement| Added parameter ``squared`` in :func:`metrics.mean_squared_error`
  to return root mean squared error.
  :pr:`13467` by :user:`Urvang Patel <urvang96>`.

- |Enhancement| Allow computing averaged metrics in the case of no true positives.
  :pr:`14595` by `Andreas Müller`_.

- |Enhancement| Multilabel metrics now supports list of lists as input.
  :pr:`14865` :user:`Srivatsan Ramesh <srivatsan-ramesh>`,
  :user:`Herilalaina Rakotoarison <herilalaina>`,
  :user:`Léonard Binet <leonardbinet>`.

- |Enhancement| :func:`metrics.median_absolute_error` now supports
  ``multioutput`` parameter.
  :pr:`14732` by :user:`Agamemnon Krasoulis <agamemnonc>`.

- |Enhancement| 'roc_auc_ovr_weighted' and 'roc_auc_ovo_weighted' can now be
  used as the :term:`scoring` parameter of model-selection tools.
  :pr:`14417` by `Thomas Fan`_.

- |Enhancement| :func:`metrics.confusion_matrix` accepts a parameters
  `normalize` allowing to normalize the confusion matrix by column, rows, or
  overall.
  :pr:`15625` by `Guillaume Lemaitre <glemaitre>`.

- |Fix| Raise a ValueError in :func:`metrics.silhouette_score` when a
  precomputed distance matrix contains non-zero diagonal entries.
  :pr:`12258` by :user:`Stephen Tierney <sjtrny>`.

- |API| ``scoring="neg_brier_score"`` should be used instead of
  ``scoring="brier_score_loss"`` which is now deprecated.
  :pr:`14898` by :user:`Stefan Matcovici <stefan-matcovici>`.

:mod:`sklearn.model_selection`
..............................

- |Efficiency| Improved performance of multimetric scoring in
  :func:`model_selection.cross_validate`,
  :class:`model_selection.GridSearchCV`, and
  :class:`model_selection.RandomizedSearchCV`. :pr:`14593` by `Thomas Fan`_.

- |Enhancement| :class:`model_selection.learning_curve` now accepts parameter
  ``return_times`` which can be used to retrieve computation times in order to
  plot model scalability (see learning_curve example).
  :pr:`13938` by :user:`Hadrien Reboul <H4dr1en>`.

- |Enhancement| :class:`model_selection.RandomizedSearchCV` now accepts lists
  of parameter distributions. :pr:`14549` by `Andreas Müller`_.

- |Fix| Reimplemented :class:`model_selection.StratifiedKFold` to fix an issue
  where one test set could be `n_classes` larger than another. Test sets should
  now be near-equally sized. :pr:`14704` by `Joel Nothman`_.

- |Fix| The `cv_results_` attribute of :class:`model_selection.GridSearchCV`
  and :class:`model_selection.RandomizedSearchCV` now only contains unfitted
  estimators. This potentially saves a lot of memory since the state of the
  estimators isn't stored. :pr:`#15096` by `Andreas Müller`_.

- |API| :class:`model_selection.KFold` and
  :class:`model_selection.StratifiedKFold` now raise a warning if
  `random_state` is set but `shuffle` is False. This will raise an error in
  0.24.

:mod:`sklearn.multioutput`
..........................

- |Fix| :class:`multioutput.MultiOutputClassifier` now has attribute
  ``classes_``. :pr:`14629` by :user:`Agamemnon Krasoulis <agamemnonc>`.

- |Fix| :class:`multioutput.MultiOutputClassifier` now has `predict_proba`
  as property and can be checked with `hasattr`.
  :issue:`15488` :pr:`15490` by :user:`Rebekah Kim <rebekahkim>`

:mod:`sklearn.naive_bayes`
...............................

- |MajorFeature| Added :class:`naive_bayes.CategoricalNB` that implements the
  Categorical Naive Bayes classifier.
  :pr:`12569` by :user:`Tim Bicker <timbicker>` and
  :user:`Florian Wilhelm <FlorianWilhelm>`.

:mod:`sklearn.neighbors`
........................

- |MajorFeature| Added :class:`neighbors.KNeighborsTransformer` and
  :class:`neighbors.RadiusNeighborsTransformer`, which transform input dataset
  into a sparse neighbors graph. They give finer control on nearest neighbors
  computations and enable easy pipeline caching for multiple use.
  :issue:`10482` by `Tom Dupre la Tour`_.

- |Feature| :class:`neighbors.KNeighborsClassifier`,
  :class:`neighbors.KNeighborsRegressor`,
  :class:`neighbors.RadiusNeighborsClassifier`,
  :class:`neighbors.RadiusNeighborsRegressor`, and
  :class:`neighbors.LocalOutlierFactor` now accept precomputed sparse
  neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`_ and
  :user:`Kumar Ashutosh <thechargedneutron>`.

- |Feature| :class:`neighbors.RadiusNeighborsClassifier` now supports
  predicting probabilities by using `predict_proba` and supports more
  outlier_label options: 'most_frequent', or different outlier_labels
  for multi-outputs.
  :pr:`9597` by :user:`Wenbo Zhao <webber26232>`.

- |Efficiency| Efficiency improvements for
  :func:`neighbors.RadiusNeighborsClassifier.predict`.
  :pr:`9597` by :user:`Wenbo Zhao <webber26232>`.

- |Fix| :class:`neighbors.KNeighborsRegressor` now throws error when
  `metric='precomputed'` and fit on non-square data.  :pr:`14336` by
  :user:`Gregory Dexter <gdex1>`.

:mod:`sklearn.neural_network`
.............................

- |Feature| Add `max_fun` parameter in
  `neural_network.BaseMultilayerPerceptron`,
  :class:`neural_network.MLPRegressor`, and
  :class:`neural_network.MLPClassifier` to give control over
  maximum number of function evaluation to not meet ``tol`` improvement.
  :issue:`9274` by :user:`Daniel Perry <daniel-perry>`.

:mod:`sklearn.pipeline`
.......................

- |Enhancement| :class:`pipeline.Pipeline` now supports :term:`score_samples` if
  the final estimator does.
  :pr:`13806` by :user:`Anaël Beaugnon <ab-anssi>`.

- |Fix| The `fit` in :class:`~pipeline.FeatureUnion` now accepts `fit_params`
  to pass to the underlying transformers. :pr:`15119` by `Adrin Jalali`_.

- |API| `None` as a transformer is now deprecated in
  :class:`pipeline.FeatureUnion`. Please use `'drop'` instead. :pr:`15053` by
  `Thomas Fan`_.

:mod:`sklearn.preprocessing`
............................

- |Efficiency| :class:`preprocessing.PolynomialFeatures` is now faster when
  the input data is dense. :pr:`13290` by :user:`Xavier Dupré <sdpython>`.

- |Enhancement| Avoid unnecessary data copy when fitting preprocessors
  :class:`preprocessing.StandardScaler`, :class:`preprocessing.MinMaxScaler`,
  :class:`preprocessing.MaxAbsScaler`, :class:`preprocessing.RobustScaler`
  and :class:`preprocessing.QuantileTransformer` which results in a slight
  performance improvement. :pr:`13987` by `Roman Yurchak`_.

- |Fix| KernelCenterer now throws error when fit on non-square
  :class:`preprocessing.KernelCenterer`
  :pr:`14336` by :user:`Gregory Dexter <gdex1>`.

:mod:`sklearn.model_selection`
..............................

- |Fix| :class:`model_selection.GridSearchCV` and
  `model_selection.RandomizedSearchCV` now supports the
  `_pairwise` property, which prevents an error during cross-validation
  for estimators with pairwise inputs (such as
  :class:`neighbors.KNeighborsClassifier` when :term:`metric` is set to
  'precomputed').
  :pr:`13925` by :user:`Isaac S. Robson <isrobson>` and :pr:`15524` by
  :user:`Xun Tang <xun-tang>`.

:mod:`sklearn.svm`
..................

- |Enhancement| :class:`svm.SVC` and :class:`svm.NuSVC` now accept a
  ``break_ties`` parameter. This parameter results in :term:`predict` breaking
  the ties according to the confidence values of :term:`decision_function`, if
  ``decision_function_shape='ovr'``, and the number of target classes > 2.
  :pr:`12557` by `Adrin Jalali`_.

- |Enhancement| SVM estimators now throw a more specific error when
  `kernel='precomputed'` and fit on non-square data.
  :pr:`14336` by :user:`Gregory Dexter <gdex1>`.

- |Fix| :class:`svm.SVC`, :class:`svm.SVR`, :class:`svm.NuSVR` and
  :class:`svm.OneClassSVM` when received values negative or zero
  for parameter ``sample_weight`` in method fit(), generated an
  invalid model. This behavior occurred only in some border scenarios.
  Now in these cases, fit() will fail with an Exception.
  :pr:`14286` by :user:`Alex Shacked <alexshacked>`.

- |Fix| The `n_support_` attribute of :class:`svm.SVR` and
  :class:`svm.OneClassSVM` was previously non-initialized, and had size 2. It
  has now size 1 with the correct value. :pr:`15099` by `Nicolas Hug`_.

- |Fix| fixed a bug in `BaseLibSVM._sparse_fit` where n_SV=0 raised a
  ZeroDivisionError. :pr:`14894` by :user:`Danna Naser <danna-naser>`.

- |Fix| The liblinear solver now supports ``sample_weight``.
  :pr:`15038` by `Guillaume Lemaitre`_.


:mod:`sklearn.tree`
...................

- |Feature| Adds minimal cost complexity pruning, controlled by ``ccp_alpha``,
  to :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`,
  :class:`tree.ExtraTreeClassifier`, :class:`tree.ExtraTreeRegressor`,
  :class:`ensemble.RandomForestClassifier`,
  :class:`ensemble.RandomForestRegressor`,
  :class:`ensemble.ExtraTreesClassifier`,
  :class:`ensemble.ExtraTreesRegressor`,
  :class:`ensemble.GradientBoostingClassifier`,
  and :class:`ensemble.GradientBoostingRegressor`.
  :pr:`12887` by `Thomas Fan`_.

- |API| ``presort`` is now deprecated in
  :class:`tree.DecisionTreeClassifier` and
  :class:`tree.DecisionTreeRegressor`, and the parameter has no effect.
  :pr:`14907` by `Adrin Jalali`_.

- |API| The ``classes_`` and ``n_classes_`` attributes of
  :class:`tree.DecisionTreeRegressor` are now deprecated. :pr:`15028` by
  :user:`Mei Guan <meiguan>`, `Nicolas Hug`_, and `Adrin Jalali`_.

:mod:`sklearn.utils`
....................

- |Feature| :func:`~utils.estimator_checks.check_estimator` can now generate
  checks by setting `generate_only=True`. Previously, running
  :func:`~utils.estimator_checks.check_estimator` will stop when the first
  check fails. With `generate_only=True`, all checks can run independently and
  report the ones that are failing. Read more in
  :ref:`rolling_your_own_estimator`. :pr:`14381` by `Thomas Fan`_.

- |Feature| Added a pytest specific decorator,
  :func:`~utils.estimator_checks.parametrize_with_checks`, to parametrize
  estimator checks for a list of estimators. :pr:`14381` by `Thomas Fan`_.

- |Feature| A new random variable, `utils.fixes.loguniform` implements a
  log-uniform random variable (e.g., for use in RandomizedSearchCV).
  For example, the outcomes ``1``, ``10`` and ``100`` are all equally likely
  for ``loguniform(1, 100)``. See :issue:`11232` by
  :user:`Scott Sievert <stsievert>` and :user:`Nathaniel Saul <sauln>`,
  and `SciPy PR 10815 <https://github.com/scipy/scipy/pull/10815>`.

- |Enhancement| `utils.safe_indexing` (now deprecated) accepts an
  ``axis`` parameter to index array-like across rows and columns. The column
  indexing can be done on NumPy array, SciPy sparse matrix, and Pandas
  DataFrame. An additional refactoring was done. :pr:`14035` and :pr:`14475`
  by `Guillaume Lemaitre`_.

- |Enhancement| :func:`utils.extmath.safe_sparse_dot` works between 3D+ ndarray
  and sparse matrix.
  :pr:`14538` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| :func:`utils.check_array` is now raising an error instead of casting
  NaN to integer.
  :pr:`14872` by `Roman Yurchak`_.

- |Fix| :func:`utils.check_array` will now correctly detect numeric dtypes in
  pandas dataframes, fixing a bug where ``float32`` was upcast to ``float64``
  unnecessarily. :pr:`15094` by `Andreas Müller`_.

- |API| The following utils have been deprecated and are now private:

  - ``choose_check_classifiers_labels``
  - ``enforce_estimator_tags_y``
  - ``mocking.MockDataFrame``
  - ``mocking.CheckingClassifier``
  - ``optimize.newton_cg``
  - ``random.random_choice_csc``
  - ``utils.choose_check_classifiers_labels``
  - ``utils.enforce_estimator_tags_y``
  - ``utils.optimize.newton_cg``
  - ``utils.random.random_choice_csc``
  - ``utils.safe_indexing``
  - ``utils.mocking``
  - ``utils.fast_dict``
  - ``utils.seq_dataset``
  - ``utils.weight_vector``
  - ``utils.fixes.parallel_helper`` (removed)
  - All of ``utils.testing`` except for ``all_estimators`` which is now in
    ``utils``.

:mod:`sklearn.isotonic`
..................................

- |Fix| Fixed a bug where :class:`isotonic.IsotonicRegression.fit` raised error
  when `X.dtype == 'float32'` and `X.dtype != y.dtype`.
  :pr:`14902` by :user:`Lucas <lostcoaster>`.

Miscellaneous
.............

- |Fix| Port `lobpcg` from SciPy which implement some bug fixes but only
  available in 1.3+.
  :pr:`13609` and :pr:`14971` by `Guillaume Lemaitre`_.

- |API| Scikit-learn now converts any input data structure implementing a
  duck array to a numpy array (using ``__array__``) to ensure consistent
  behavior instead of relying on ``__array_function__`` (see `NEP 18
  <https://numpy.org/neps/nep-0018-array-function-protocol.html>`_).
  :pr:`14702` by `Andreas Müller`_.

- |API| Replace manual checks with ``check_is_fitted``. Errors thrown when
  using a non-fitted estimators are now more uniform.
  :pr:`13013` by :user:`Agamemnon Krasoulis <agamemnonc>`.

Changes to estimator checks
---------------------------

These changes mostly affect library developers.

- Estimators are now expected to raise a ``NotFittedError`` if ``predict`` or
  ``transform`` is called before ``fit``; previously an ``AttributeError`` or
  ``ValueError`` was acceptable.
  :pr:`13013` by by :user:`Agamemnon Krasoulis <agamemnonc>`.

- Binary only classifiers are now supported in estimator checks.
  Such classifiers need to have the `binary_only=True` estimator tag.
  :pr:`13875` by `Trevor Stephens`_.

- Estimators are expected to convert input data (``X``, ``y``,
  ``sample_weights``) to :class:`numpy.ndarray` and never call
  ``__array_function__`` on the original datatype that is passed (see `NEP 18
  <https://numpy.org/neps/nep-0018-array-function-protocol.html>`_).
  :pr:`14702` by `Andreas Müller`_.

- `requires_positive_X` estimator tag (for models that require
  X to be non-negative) is now used by :meth:`utils.estimator_checks.check_estimator`
  to make sure a proper error message is raised if X contains some negative entries.
  :pr:`14680` by :user:`Alex Gramfort <agramfort>`.

- Added check that pairwise estimators raise error on non-square data
  :pr:`14336` by :user:`Gregory Dexter <gdex1>`.

- Added two common multioutput estimator tests
  `utils.estimator_checks.check_classifier_multioutput` and
  `utils.estimator_checks.check_regressor_multioutput`.
  :pr:`13392` by :user:`Rok Mihevc <rok>`.

- |Fix| Added ``check_transformer_data_not_an_array`` to checks where missing

- |Fix| The estimators tags resolution now follows the regular MRO. They used
  to be overridable only once. :pr:`14884` by `Andreas Müller`_.


.. rubric:: Code and documentation contributors

Thanks to everyone who has contributed to the maintenance and improvement of the
project since version 0.21, including:

Aaron Alphonsus, Abbie Popa, Abdur-Rahmaan Janhangeer, abenbihi, Abhinav Sagar,
Abhishek Jana, Abraham K. Lagat, Adam J. Stewart, Aditya Vyas, Adrin Jalali,
Agamemnon Krasoulis, Alec Peters, Alessandro Surace, Alexandre de Siqueira,
Alexandre Gramfort, alexgoryainov, Alex Henrie, Alex Itkes, alexshacked, Allen
Akinkunle, Anaël Beaugnon, Anders Kaseorg, Andrea Maldonado, Andrea Navarrete,
Andreas Mueller, Andreas Schuderer, Andrew Nystrom, Angela Ambroz, Anisha
Keshavan, Ankit Jha, Antonio Gutierrez, Anuja Kelkar, Archana Alva,
arnaudstiegler, arpanchowdhry, ashimb9, Ayomide Bamidele, Baran Buluttekin,
barrycg, Bharat Raghunathan, Bill Mill, Biswadip Mandal, blackd0t, Brian G.
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