.. include:: _contributors.rst

.. currentmodule:: sklearn

.. _changes_0_20_4:

Version 0.20.4
==============

**July 30, 2019**

This is a bug-fix release with some bug fixes applied to version 0.20.3.

Changelog
---------

The bundled version of joblib was upgraded from 0.13.0 to 0.13.2.

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

- |Fix| Fixed a bug in :class:`cluster.KMeans` where KMeans++ initialisation
  could rarely result in an IndexError. :issue:`11756` by `Joel Nothman`_.

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

- |Fix| Fixed an issue in :class:`compose.ColumnTransformer` where using
  DataFrames whose column order differs between :func:``fit`` and
  :func:``transform`` could lead to silently passing incorrect columns to the
  ``remainder`` transformer.
  :pr:`14237` by `Andreas Schuderer <schuderer>`.

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

- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical
  stability when `Y` is close to zero. :pr:`13903` by `Thomas Fan`_.


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

- |Fix| Fixed a bug where :class:`model_selection.StratifiedKFold`
  shuffles each class's samples with the same ``random_state``,
  making ``shuffle=True`` ineffective.
  :issue:`13124` by :user:`Hanmin Qin <qinhanmin2014>`.

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

- |Fix| Fixed a bug in :class:`neighbors.KernelDensity` which could not be
  restored from a pickle if ``sample_weight`` had been used.
  :issue:`13772` by :user:`Aditya Vyas <aditya1702>`.

 .. _changes_0_20_3:

Version 0.20.3
==============

**March 1, 2019**

This is a bug-fix release with some minor documentation improvements and
enhancements to features released in 0.20.0.

Changelog
---------

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

- |Fix| Fixed a bug in :class:`cluster.KMeans` where computation was single
  threaded when `n_jobs > 1` or `n_jobs = -1`.
  :issue:`12949` by :user:`Prabakaran Kumaresshan <nixphix>`.

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

- |Fix| Fixed a bug in :class:`compose.ColumnTransformer` to handle
  negative indexes in the columns list of the transformers.
  :issue:`12946` by :user:`Pierre Tallotte <pierretallotte>`.

:mod:`sklearn.covariance`
.........................

- |Fix| Fixed a regression in :func:`covariance.graphical_lasso` so that
  the case `n_features=2` is handled correctly. :issue:`13276` by
  :user:`Aurélien Bellet <bellet>`.

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

- |Fix| Fixed a bug in :func:`decomposition.sparse_encode` where computation was single
  threaded when `n_jobs > 1` or `n_jobs = -1`.
  :issue:`13005` by :user:`Prabakaran Kumaresshan <nixphix>`.

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

- |Efficiency| :func:`sklearn.datasets.fetch_openml` now loads data by
  streaming, avoiding high memory usage.  :issue:`13312` by `Joris Van den
  Bossche`_.

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

- |Fix| Fixed a bug in :class:`feature_extraction.text.CountVectorizer` which
  would result in the sparse feature matrix having conflicting `indptr` and
  `indices` precisions under very large vocabularies. :issue:`11295` by
  :user:`Gabriel Vacaliuc <gvacaliuc>`.

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

- |Fix| add support for non-numeric data in
  :class:`sklearn.impute.MissingIndicator` which was not supported while
  :class:`sklearn.impute.SimpleImputer` was supporting this for some
  imputation strategies.
  :issue:`13046` by :user:`Guillaume Lemaitre <glemaitre>`.

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

- |Fix| Fixed a bug in :class:`linear_model.MultiTaskElasticNet` and
  :class:`linear_model.MultiTaskLasso` which were breaking when
  ``warm_start = True``. :issue:`12360` by :user:`Aakanksha Joshi <joaak>`.

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

- |Fix| Fixed a bug in :class:`preprocessing.KBinsDiscretizer` where
  ``strategy='kmeans'`` fails with an error during transformation due to unsorted
  bin edges. :issue:`13134` by :user:`Sandro Casagrande <SandroCasagrande>`.

- |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where the
  deprecation of ``categorical_features`` was handled incorrectly in
  combination with ``handle_unknown='ignore'``.
  :issue:`12881` by `Joris Van den Bossche`_.

- |Fix| Bins whose width are too small (i.e., <= 1e-8) are removed
  with a warning in :class:`preprocessing.KBinsDiscretizer`.
  :issue:`13165` by :user:`Hanmin Qin <qinhanmin2014>`.

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

- |FIX| Fixed a bug in :class:`svm.SVC`, :class:`svm.NuSVC`, :class:`svm.SVR`,
  :class:`svm.NuSVR` and :class:`svm.OneClassSVM` where the ``scale`` option
  of parameter ``gamma`` is erroneously defined as
  ``1 / (n_features * X.std())``. It's now defined as
  ``1 / (n_features * X.var())``.
  :issue:`13221` by :user:`Hanmin Qin <qinhanmin2014>`.

Code and Documentation Contributors
-----------------------------------

With thanks to:

Adrin Jalali, Agamemnon Krasoulis, Albert Thomas, Andreas Mueller, Aurélien
Bellet, bertrandhaut, Bharat Raghunathan, Dowon, Emmanuel Arias, Fibinse
Xavier, Finn O'Shea, Gabriel Vacaliuc, Gael Varoquaux, Guillaume Lemaitre,
Hanmin Qin, joaak, Joel Nothman, Joris Van den Bossche, Jérémie Méhault, kms15,
Kossori Aruku, Lakshya KD, maikia, Manuel López-Ibáñez, Marco Gorelli,
MarcoGorelli, mferrari3, Mickaël Schoentgen, Nicolas Hug, pavlos kallis, Pierre
Glaser, pierretallotte, Prabakaran Kumaresshan, Reshama Shaikh, Rohit Kapoor,
Roman Yurchak, SandroCasagrande, Tashay Green, Thomas Fan, Vishaal Kapoor,
Zhuyi Xue, Zijie (ZJ) Poh

.. _changes_0_20_2:

Version 0.20.2
==============

**December 20, 2018**

This is a bug-fix release with some minor documentation improvements and
enhancements to features released in 0.20.0.

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.

- :mod:`sklearn.neighbors` when ``metric=='jaccard'`` (bug fix)
- use of ``'seuclidean'`` or ``'mahalanobis'`` metrics in some cases (bug fix)

Changelog
---------

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

- |Fix| Fixed an issue in :func:`compose.make_column_transformer` which raises
  unexpected error when columns is pandas Index or pandas Series.
  :issue:`12704` by :user:`Hanmin Qin <qinhanmin2014>`.

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

- |Fix| Fixed a bug in :func:`metrics.pairwise_distances` and
  :func:`metrics.pairwise_distances_chunked` where parameters ``V`` of
  ``"seuclidean"`` and ``VI`` of ``"mahalanobis"`` metrics were computed after
  the data was split into chunks instead of being pre-computed on whole data.
  :issue:`12701` by :user:`Jeremie du Boisberranger <jeremiedbb>`.

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

- |Fix| Fixed `sklearn.neighbors.DistanceMetric` jaccard distance
  function to return 0 when two all-zero vectors are compared.
  :issue:`12685` by :user:`Thomas Fan <thomasjpfan>`.

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

- |Fix| Calling :func:`utils.check_array` on `pandas.Series` with categorical
  data, which raised an error in 0.20.0, now returns the expected output again.
  :issue:`12699` by `Joris Van den Bossche`_.

Code and Documentation Contributors
-----------------------------------

With thanks to:


adanhawth, Adrin Jalali, Albert Thomas, Andreas Mueller, Dan Stine, Feda Curic,
Hanmin Qin, Jan S, jeremiedbb, Joel Nothman, Joris Van den Bossche,
josephsalmon, Katrin Leinweber, Loic Esteve, Muhammad Hassaan Rafique, Nicolas
Hug, Olivier Grisel, Paul Paczuski, Reshama Shaikh, Sam Waterbury, Shivam
Kotwalia, Thomas Fan

.. _changes_0_20_1:

Version 0.20.1
==============

**November 21, 2018**

This is a bug-fix release with some minor documentation improvements and
enhancements to features released in 0.20.0. Note that we also include some
API changes in this release, so you might get some extra warnings after
updating from 0.20.0 to 0.20.1.

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:`decomposition.IncrementalPCA` (bug fix)

Changelog
---------

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

- |Efficiency| make :class:`cluster.MeanShift` no longer try to do nested
  parallelism as the overhead would hurt performance significantly when
  ``n_jobs > 1``.
  :issue:`12159` by :user:`Olivier Grisel <ogrisel>`.

- |Fix| Fixed a bug in :class:`cluster.DBSCAN` with precomputed sparse neighbors
  graph, which would add explicitly zeros on the diagonal even when already
  present. :issue:`12105` by `Tom Dupre la Tour`_.

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

- |Fix| Fixed an issue in :class:`compose.ColumnTransformer` when stacking
  columns with types not convertible to a numeric.
  :issue:`11912` by :user:`Adrin Jalali <adrinjalali>`.

- |API| :class:`compose.ColumnTransformer` now applies the ``sparse_threshold``
  even if all transformation results are sparse. :issue:`12304` by `Andreas
  Müller`_.

- |API| :func:`compose.make_column_transformer` now expects
  ``(transformer, columns)`` instead of ``(columns, transformer)`` to keep
  consistent with :class:`compose.ColumnTransformer`.
  :issue:`12339` by :user:`Adrin Jalali <adrinjalali>`.

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

- |Fix| :func:`datasets.fetch_openml` to correctly use the local cache.
  :issue:`12246` by :user:`Jan N. van Rijn <janvanrijn>`.

- |Fix| :func:`datasets.fetch_openml` to correctly handle ignore attributes and
  row id attributes. :issue:`12330` by :user:`Jan N. van Rijn <janvanrijn>`.

- |Fix| Fixed integer overflow in :func:`datasets.make_classification`
  for values of ``n_informative`` parameter larger than 64.
  :issue:`10811` by :user:`Roman Feldbauer <VarIr>`.

- |Fix| Fixed olivetti faces dataset ``DESCR`` attribute to point to the right
  location in :func:`datasets.fetch_olivetti_faces`. :issue:`12441` by
  :user:`Jérémie du Boisberranger <jeremiedbb>`

- |Fix| :func:`datasets.fetch_openml` to retry downloading when reading
  from local cache fails. :issue:`12517` by :user:`Thomas Fan <thomasjpfan>`.

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

- |Fix| Fixed a regression in :class:`decomposition.IncrementalPCA` where
  0.20.0 raised an error if the number of samples in the final batch for
  fitting IncrementalPCA was smaller than n_components.
  :issue:`12234` by :user:`Ming Li <minggli>`.

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

- |Fix| Fixed a bug mostly affecting :class:`ensemble.RandomForestClassifier`
  where ``class_weight='balanced_subsample'`` failed with more than 32 classes.
  :issue:`12165` by `Joel Nothman`_.

- |Fix| Fixed a bug affecting :class:`ensemble.BaggingClassifier`,
  :class:`ensemble.BaggingRegressor` and :class:`ensemble.IsolationForest`,
  where ``max_features`` was sometimes rounded down to zero.
  :issue:`12388` by :user:`Connor Tann <Connossor>`.

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

- |Fix| Fixed a regression in v0.20.0 where
  :func:`feature_extraction.text.CountVectorizer` and other text vectorizers
  could error during stop words validation with custom preprocessors
  or tokenizers. :issue:`12393` by `Roman Yurchak`_.

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

- |Fix| :class:`linear_model.SGDClassifier` and variants
  with ``early_stopping=True`` would not use a consistent validation
  split in the multiclass case and this would cause a crash when using
  those estimators as part of parallel parameter search or cross-validation.
  :issue:`12122` by :user:`Olivier Grisel <ogrisel>`.

- |Fix| Fixed a bug affecting :class:`linear_model.SGDClassifier` in the multiclass
  case. Each one-versus-all step is run in a :class:`joblib.Parallel` call and
  mutating a common parameter, causing a segmentation fault if called within a
  backend using processes and not threads. We now use ``require=sharedmem``
  at the :class:`joblib.Parallel` instance creation. :issue:`12518` by
  :user:`Pierre Glaser <pierreglaser>` and :user:`Olivier Grisel <ogrisel>`.

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

- |Fix| Fixed a bug in `metrics.pairwise.pairwise_distances_argmin_min`
  which returned the square root of the distance when the metric parameter was
  set to "euclidean". :issue:`12481` by
  :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| Fixed a bug in `metrics.pairwise.pairwise_distances_chunked`
  which didn't ensure the diagonal is zero for euclidean distances.
  :issue:`12612` by :user:`Andreas Müller <amueller>`.

- |API| The `metrics.calinski_harabaz_score` has been renamed to
  :func:`metrics.calinski_harabasz_score` and will be removed in version 0.23.
  :issue:`12211` by :user:`Lisa Thomas <LisaThomas9>`,
  :user:`Mark Hannel <markhannel>` and :user:`Melissa Ferrari <mferrari3>`.

:mod:`sklearn.mixture`
........................

- |Fix| Ensure that the ``fit_predict`` method of
  :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`
  always yield assignments consistent with ``fit`` followed by ``predict`` even
  if the convergence criterion is too loose or not met. :issue:`12451`
  by :user:`Olivier Grisel <ogrisel>`.

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

- |Fix| force the parallelism backend to :code:`threading` for
  :class:`neighbors.KDTree` and :class:`neighbors.BallTree` in Python 2.7 to
  avoid pickling errors caused by the serialization of their methods.
  :issue:`12171` by :user:`Thomas Moreau <tomMoral>`.

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

- |Fix| Fixed bug in :class:`preprocessing.OrdinalEncoder` when passing
  manually specified categories. :issue:`12365` by `Joris Van den Bossche`_.

- |Fix| Fixed bug in :class:`preprocessing.KBinsDiscretizer` where the
  ``transform`` method mutates the ``_encoder`` attribute. The ``transform``
  method is now thread safe. :issue:`12514` by
  :user:`Hanmin Qin <qinhanmin2014>`.

- |Fix| Fixed a bug in :class:`preprocessing.PowerTransformer` where the
  Yeo-Johnson transform was incorrect for lambda parameters outside of `[0, 2]`
  :issue:`12522` by :user:`Nicolas Hug<NicolasHug>`.

- |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where transform
  failed when set to ignore unknown numpy strings of different lengths
  :issue:`12471` by :user:`Gabriel Marzinotto<GMarzinotto>`.

- |API| The default value of the :code:`method` argument in
  :func:`preprocessing.power_transform` will be changed from :code:`box-cox`
  to :code:`yeo-johnson` to match :class:`preprocessing.PowerTransformer`
  in version 0.23. A FutureWarning is raised when the default value is used.
  :issue:`12317` by :user:`Eric Chang <chang>`.

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

- |Fix| Use float64 for mean accumulator to avoid floating point
  precision issues in :class:`preprocessing.StandardScaler` and
  :class:`decomposition.IncrementalPCA` when using float32 datasets.
  :issue:`12338` by :user:`bauks <bauks>`.

- |Fix| Calling :func:`utils.check_array` on `pandas.Series`, which
  raised an error in 0.20.0, now returns the expected output again.
  :issue:`12625` by `Andreas Müller`_

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

- |Fix| When using site joblib by setting the environment variable
  `SKLEARN_SITE_JOBLIB`, added compatibility with joblib 0.11 in addition
  to 0.12+. :issue:`12350` by `Joel Nothman`_ and `Roman Yurchak`_.

- |Fix| Make sure to avoid raising ``FutureWarning`` when calling
  ``np.vstack`` with numpy 1.16 and later (use list comprehensions
  instead of generator expressions in many locations of the scikit-learn
  code base). :issue:`12467` by :user:`Olivier Grisel <ogrisel>`.

- |API| Removed all mentions of ``sklearn.externals.joblib``, and deprecated
  joblib methods exposed in ``sklearn.utils``, except for
  :func:`utils.parallel_backend` and :func:`utils.register_parallel_backend`,
  which allow users to configure parallel computation in scikit-learn.
  Other functionalities are part of `joblib <https://joblib.readthedocs.io/>`_.
  package and should be used directly, by installing it.
  The goal of this change is to prepare for
  unvendoring joblib in future version of scikit-learn.
  :issue:`12345` by :user:`Thomas Moreau <tomMoral>`

Code and Documentation Contributors
-----------------------------------

With thanks to:

^__^, Adrin Jalali, Andrea Navarrete, Andreas Mueller,
bauks, BenjaStudio, Cheuk Ting Ho, Connossor,
Corey Levinson, Dan Stine, daten-kieker, Denis Kataev,
Dillon Gardner, Dmitry Vukolov, Dougal J. Sutherland, Edward J Brown,
Eric Chang, Federico Caselli, Gabriel Marzinotto, Gael Varoquaux,
GauravAhlawat, Gustavo De Mari Pereira, Hanmin Qin, haroldfox,
JackLangerman, Jacopo Notarstefano, janvanrijn, jdethurens,
jeremiedbb, Joel Nothman, Joris Van den Bossche, Koen,
Kushal Chauhan, Lee Yi Jie Joel, Lily Xiong, mail-liam,
Mark Hannel, melsyt, Ming Li, Nicholas Smith,
Nicolas Hug, Nikolay Shebanov, Oleksandr Pavlyk, Olivier Grisel,
Peter Hausamann, Pierre Glaser, Pulkit Maloo, Quentin Batista,
Radostin Stoyanov, Ramil Nugmanov, Rebekah Kim, Reshama Shaikh,
Rohan Singh, Roman Feldbauer, Roman Yurchak, Roopam Sharma,
Sam Waterbury, Scott Lowe, Sebastian Raschka, Stephen Tierney,
SylvainLan, TakingItCasual, Thomas Fan, Thomas Moreau,
Tom Dupré la Tour, Tulio Casagrande, Utkarsh Upadhyay, Xing Han Lu,
Yaroslav Halchenko, Zach Miller


.. _changes_0_20:

Version 0.20.0
==============

**September 25, 2018**

This release packs in a mountain of bug fixes, features and enhancements for
the Scikit-learn library, and improvements to the documentation and examples.
Thanks to our contributors!

This release is dedicated to the memory of Raghav Rajagopalan.

.. warning::

    Version 0.20 is the last version of scikit-learn to support Python 2.7 and Python 3.4.
    Scikit-learn 0.21 will require Python 3.5 or higher.

Highlights
----------

We have tried to improve our support for common data-science use-cases
including missing values, categorical variables, heterogeneous data, and
features/targets with unusual distributions.
Missing values in features, represented by NaNs, are now accepted in
column-wise preprocessing such as scalers. Each feature is fitted disregarding
NaNs, and data containing NaNs can be transformed. The new :mod:`sklearn.impute`
module provides estimators for learning despite missing data.

:class:`~compose.ColumnTransformer` handles the case where different features
or columns of a pandas.DataFrame need different preprocessing.
String or pandas Categorical columns can now be encoded with
:class:`~preprocessing.OneHotEncoder` or
:class:`~preprocessing.OrdinalEncoder`.

:class:`~compose.TransformedTargetRegressor` helps when the regression target
needs to be transformed to be modeled. :class:`~preprocessing.PowerTransformer`
and :class:`~preprocessing.KBinsDiscretizer` join
:class:`~preprocessing.QuantileTransformer` as non-linear transformations.

Beyond this, we have added :term:`sample_weight` support to several estimators
(including :class:`~cluster.KMeans`, :class:`~linear_model.BayesianRidge` and
:class:`~neighbors.KernelDensity`) and improved stopping criteria in others
(including :class:`~neural_network.MLPRegressor`,
:class:`~ensemble.GradientBoostingRegressor` and
:class:`~linear_model.SGDRegressor`).

This release is also the first to be accompanied by a :ref:`glossary` developed
by `Joel Nothman`_. The glossary is a reference resource to help users and
contributors become familiar with the terminology and conventions used in
Scikit-learn.

Sorry if your contribution didn't make it into the highlights. There's a lot
here...

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.MeanShift` (bug fix)
- :class:`decomposition.IncrementalPCA` in Python 2 (bug fix)
- :class:`decomposition.SparsePCA` (bug fix)
- :class:`ensemble.GradientBoostingClassifier` (bug fix affecting feature importances)
- :class:`isotonic.IsotonicRegression` (bug fix)
- :class:`linear_model.ARDRegression` (bug fix)
- :class:`linear_model.LogisticRegressionCV` (bug fix)
- :class:`linear_model.OrthogonalMatchingPursuit` (bug fix)
- :class:`linear_model.PassiveAggressiveClassifier` (bug fix)
- :class:`linear_model.PassiveAggressiveRegressor` (bug fix)
- :class:`linear_model.Perceptron` (bug fix)
- :class:`linear_model.SGDClassifier` (bug fix)
- :class:`linear_model.SGDRegressor` (bug fix)
- :class:`metrics.roc_auc_score` (bug fix)
- :class:`metrics.roc_curve` (bug fix)
- `neural_network.BaseMultilayerPerceptron` (bug fix)
- :class:`neural_network.MLPClassifier` (bug fix)
- :class:`neural_network.MLPRegressor` (bug fix)
- The v0.19.0 release notes failed to mention a backwards incompatibility with
  :class:`model_selection.StratifiedKFold` when ``shuffle=True`` due to
  :issue:`7823`.

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.)

Known Major Bugs
----------------

* :issue:`11924`: :class:`linear_model.LogisticRegressionCV` with
  `solver='lbfgs'` and `multi_class='multinomial'` may be non-deterministic or
  otherwise broken on macOS. This appears to be the case on Travis CI servers,
  but has not been confirmed on personal MacBooks! This issue has been present
  in previous releases.

* :issue:`9354`: :func:`metrics.pairwise.euclidean_distances` (which is used
  several times throughout the library) gives results with poor precision,
  which particularly affects its use with 32-bit float inputs. This became
  more problematic in versions 0.18 and 0.19 when some algorithms were changed
  to avoid casting 32-bit data into 64-bit.

Changelog
---------

Support for Python 3.3 has been officially dropped.


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

- |MajorFeature| :class:`cluster.AgglomerativeClustering` now supports Single
  Linkage clustering via ``linkage='single'``. :issue:`9372` by :user:`Leland
  McInnes <lmcinnes>` and :user:`Steve Astels <sastels>`.

- |Feature| :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans` now support
  sample weights via new parameter ``sample_weight`` in ``fit`` function.
  :issue:`10933` by :user:`Johannes Hansen <jnhansen>`.

- |Efficiency| :class:`cluster.KMeans`, :class:`cluster.MiniBatchKMeans` and
  :func:`cluster.k_means` passed with ``algorithm='full'`` now enforces
  row-major ordering, improving runtime.
  :issue:`10471` by :user:`Gaurav Dhingra <gxyd>`.

- |Efficiency| :class:`cluster.DBSCAN` now is parallelized according to ``n_jobs``
  regardless of ``algorithm``.
  :issue:`8003` by :user:`Joël Billaud <recamshak>`.

- |Enhancement| :class:`cluster.KMeans` now gives a warning if the number of
  distinct clusters found is smaller than ``n_clusters``. This may occur when
  the number of distinct points in the data set is actually smaller than the
  number of cluster one is looking for.
  :issue:`10059` by :user:`Christian Braune <christianbraune79>`.

- |Fix| Fixed a bug where the ``fit`` method of
  :class:`cluster.AffinityPropagation` stored cluster
  centers as 3d array instead of 2d array in case of non-convergence. For the
  same class, fixed undefined and arbitrary behavior in case of training data
  where all samples had equal similarity.
  :issue:`9612`. By :user:`Jonatan Samoocha <jsamoocha>`.

- |Fix| Fixed a bug in :func:`cluster.spectral_clustering` where the normalization of
  the spectrum was using a division instead of a multiplication. :issue:`8129`
  by :user:`Jan Margeta <jmargeta>`, :user:`Guillaume Lemaitre <glemaitre>`,
  and :user:`Devansh D. <devanshdalal>`.

- |Fix| Fixed a bug in `cluster.k_means_elkan` where the returned
  ``iteration`` was 1 less than the correct value. Also added the missing
  ``n_iter_`` attribute in the docstring of :class:`cluster.KMeans`.
  :issue:`11353` by :user:`Jeremie du Boisberranger <jeremiedbb>`.

- |Fix| Fixed a bug in :func:`cluster.mean_shift` where the assigned labels
  were not deterministic if there were multiple clusters with the same
  intensities.
  :issue:`11901` by :user:`Adrin Jalali <adrinjalali>`.

- |API| Deprecate ``pooling_func`` unused parameter in
  :class:`cluster.AgglomerativeClustering`.
  :issue:`9875` by :user:`Kumar Ashutosh <thechargedneutron>`.


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

- New module.

- |MajorFeature| Added :class:`compose.ColumnTransformer`, which allows to
  apply different transformers to different columns of arrays or pandas
  DataFrames. :issue:`9012` by `Andreas Müller`_ and `Joris Van den Bossche`_,
  and :issue:`11315` by :user:`Thomas Fan <thomasjpfan>`.

- |MajorFeature| Added the :class:`compose.TransformedTargetRegressor` which
  transforms the target y before fitting a regression model. The predictions
  are mapped back to the original space via an inverse transform. :issue:`9041`
  by `Andreas Müller`_ and :user:`Guillaume Lemaitre <glemaitre>`.



:mod:`sklearn.covariance`
.........................

- |Efficiency| Runtime improvements to :class:`covariance.GraphicalLasso`.
  :issue:`9858` by :user:`Steven Brown <stevendbrown>`.

- |API| The `covariance.graph_lasso`,
  `covariance.GraphLasso` and `covariance.GraphLassoCV` have been
  renamed to :func:`covariance.graphical_lasso`,
  :class:`covariance.GraphicalLasso` and :class:`covariance.GraphicalLassoCV`
  respectively and will be removed in version 0.22.
  :issue:`9993` by :user:`Artiem Krinitsyn <artiemq>`


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

- |MajorFeature| Added :func:`datasets.fetch_openml` to fetch datasets from
  `OpenML <https://openml.org>`_. OpenML is a free, open data sharing platform
  and will be used instead of mldata as it provides better service availability.
  :issue:`9908` by `Andreas Müller`_ and :user:`Jan N. van Rijn <janvanrijn>`.

- |Feature| In :func:`datasets.make_blobs`, one can now pass a list to the
  ``n_samples`` parameter to indicate the number of samples to generate per
  cluster. :issue:`8617` by :user:`Maskani Filali Mohamed <maskani-moh>` and
  :user:`Konstantinos Katrioplas <kkatrio>`.

- |Feature| Add ``filename`` attribute to :mod:`sklearn.datasets` that have a CSV file.
  :issue:`9101` by :user:`alex-33 <alex-33>`
  and :user:`Maskani Filali Mohamed <maskani-moh>`.

- |Feature| ``return_X_y`` parameter has been added to several dataset loaders.
  :issue:`10774` by :user:`Chris Catalfo <ccatalfo>`.

- |Fix| Fixed a bug in `datasets.load_boston` which had a wrong data
  point. :issue:`10795` by :user:`Takeshi Yoshizawa <tarcusx>`.

- |Fix| Fixed a bug in :func:`datasets.load_iris` which had two wrong data points.
  :issue:`11082` by :user:`Sadhana Srinivasan <rotuna>`
  and :user:`Hanmin Qin <qinhanmin2014>`.

- |Fix| Fixed a bug in :func:`datasets.fetch_kddcup99`, where data were not
  properly shuffled. :issue:`9731` by `Nicolas Goix`_.

- |Fix| Fixed a bug in :func:`datasets.make_circles`, where no odd number of
  data points could be generated. :issue:`10045` by :user:`Christian Braune
  <christianbraune79>`.

- |API| Deprecated `sklearn.datasets.fetch_mldata` to be removed in
  version 0.22. mldata.org is no longer operational. Until removal it will
  remain possible to load cached datasets. :issue:`11466` by `Joel Nothman`_.

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

- |Feature| :func:`decomposition.dict_learning` functions and models now
  support positivity constraints. This applies to the dictionary and sparse
  code. :issue:`6374` by :user:`John Kirkham <jakirkham>`.

- |Feature| |Fix| :class:`decomposition.SparsePCA` now exposes
  ``normalize_components``. When set to True, the train and test data are
  centered with the train mean respectively during the fit phase and the
  transform phase. This fixes the behavior of SparsePCA. When set to False,
  which is the default, the previous abnormal behaviour still holds. The False
  value is for backward compatibility and should not be used. :issue:`11585`
  by :user:`Ivan Panico <FollowKenny>`.

- |Efficiency| Efficiency improvements in :func:`decomposition.dict_learning`.
  :issue:`11420` and others by :user:`John Kirkham <jakirkham>`.

- |Fix| Fix for uninformative error in :class:`decomposition.IncrementalPCA`:
  now an error is raised if the number of components is larger than the
  chosen batch size. The ``n_components=None`` case was adapted accordingly.
  :issue:`6452`. By :user:`Wally Gauze <wallygauze>`.

- |Fix| Fixed a bug where the ``partial_fit`` method of
  :class:`decomposition.IncrementalPCA` used integer division instead of float
  division on Python 2.
  :issue:`9492` by :user:`James Bourbeau <jrbourbeau>`.

- |Fix| In :class:`decomposition.PCA` selecting a n_components parameter greater
  than the number of samples now raises an error. Similarly, the
  ``n_components=None`` case now selects the minimum of ``n_samples`` and
  ``n_features``.
  :issue:`8484` by :user:`Wally Gauze <wallygauze>`.

- |Fix| Fixed a bug in :class:`decomposition.PCA` where users will get
  unexpected error with large datasets when ``n_components='mle'`` on Python 3
  versions.
  :issue:`9886` by :user:`Hanmin Qin <qinhanmin2014>`.

- |Fix| Fixed an underflow in calculating KL-divergence for
  :class:`decomposition.NMF` :issue:`10142` by `Tom Dupre la Tour`_.

- |Fix| Fixed a bug in :class:`decomposition.SparseCoder` when running OMP
  sparse coding in parallel using read-only memory mapped datastructures.
  :issue:`5956` by :user:`Vighnesh Birodkar <vighneshbirodkar>` and
  :user:`Olivier Grisel <ogrisel>`.


:mod:`sklearn.discriminant_analysis`
....................................

- |Efficiency| Memory usage improvement for `_class_means` and
  `_class_cov` in :mod:`sklearn.discriminant_analysis`. :issue:`10898` by
  :user:`Nanxin Chen <bobchennan>`.


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

- |Feature| :class:`dummy.DummyRegressor` now has a ``return_std`` option in its
  ``predict`` method. The returned standard deviations will be zeros.

- |Feature| :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor` now
  only require X to be an object with finite length or shape. :issue:`9832` by
  :user:`Vrishank Bhardwaj <vrishank97>`.

- |Feature| :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor`
  can now be scored without supplying test samples.
  :issue:`11951` by :user:`Rüdiger Busche <JarnoRFB>`.


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

- |Feature| :class:`ensemble.BaggingRegressor` and
  :class:`ensemble.BaggingClassifier` can now be fit with missing/non-finite
  values in X and/or multi-output Y to support wrapping pipelines that perform
  their own imputation. :issue:`9707` by :user:`Jimmy Wan <jimmywan>`.

- |Feature| :class:`ensemble.GradientBoostingClassifier` and
  :class:`ensemble.GradientBoostingRegressor` now support early stopping
  via ``n_iter_no_change``, ``validation_fraction`` and ``tol``. :issue:`7071`
  by `Raghav RV`_

- |Feature| Added ``named_estimators_`` parameter in
  :class:`ensemble.VotingClassifier` to access fitted estimators.
  :issue:`9157` by :user:`Herilalaina Rakotoarison <herilalaina>`.

- |Fix| Fixed a bug when fitting :class:`ensemble.GradientBoostingClassifier` or
  :class:`ensemble.GradientBoostingRegressor` with ``warm_start=True`` which
  previously raised a segmentation fault due to a non-conversion of CSC matrix
  into CSR format expected by ``decision_function``. Similarly, Fortran-ordered
  arrays are converted to C-ordered arrays in the dense case. :issue:`9991` by
  :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| Fixed a bug in :class:`ensemble.GradientBoostingRegressor`
  and :class:`ensemble.GradientBoostingClassifier` to have
  feature importances summed and then normalized, rather than normalizing on a
  per-tree basis. The previous behavior over-weighted the Gini importance of
  features that appear in later stages. This issue only affected feature
  importances. :issue:`11176` by :user:`Gil Forsyth <gforsyth>`.

- |API| The default value of the ``n_estimators`` parameter of
  :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`,
  :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`,
  and :class:`ensemble.RandomTreesEmbedding` will change from 10 in version 0.20
  to 100 in 0.22. A FutureWarning is raised when the default value is used.
  :issue:`11542` by :user:`Anna Ayzenshtat <annaayzenshtat>`.

- |API| Classes derived from `ensemble.BaseBagging`. The attribute
  ``estimators_samples_`` will return a list of arrays containing the indices
  selected for each bootstrap instead of a list of arrays containing the mask
  of the samples selected for each bootstrap. Indices allows to repeat samples
  while mask does not allow this functionality.
  :issue:`9524` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| `ensemble.BaseBagging` where one could not deterministically
  reproduce ``fit`` result using the object attributes when ``random_state``
  is set. :issue:`9723` by :user:`Guillaume Lemaitre <glemaitre>`.


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

- |Feature| Enable the call to `get_feature_names` in unfitted
  :class:`feature_extraction.text.CountVectorizer` initialized with a
  vocabulary. :issue:`10908` by :user:`Mohamed Maskani <maskani-moh>`.

- |Enhancement| ``idf_`` can now be set on a
  :class:`feature_extraction.text.TfidfTransformer`.
  :issue:`10899` by :user:`Sergey Melderis <serega>`.

- |Fix| Fixed a bug in :func:`feature_extraction.image.extract_patches_2d` which
  would throw an exception if ``max_patches`` was greater than or equal to the
  number of all possible patches rather than simply returning the number of
  possible patches. :issue:`10101` by :user:`Varun Agrawal <varunagrawal>`

- |Fix| Fixed a bug in :class:`feature_extraction.text.CountVectorizer`,
  :class:`feature_extraction.text.TfidfVectorizer`,
  :class:`feature_extraction.text.HashingVectorizer` to support 64 bit sparse
  array indexing necessary to process large datasets with more than 2·10⁹ tokens
  (words or n-grams). :issue:`9147` by :user:`Claes-Fredrik Mannby <mannby>`
  and `Roman Yurchak`_.

- |Fix| Fixed bug in :class:`feature_extraction.text.TfidfVectorizer` which
  was ignoring the parameter ``dtype``. In addition,
  :class:`feature_extraction.text.TfidfTransformer` will preserve ``dtype``
  for floating and raise a warning if ``dtype`` requested is integer.
  :issue:`10441` by :user:`Mayur Kulkarni <maykulkarni>` and
  :user:`Guillaume Lemaitre <glemaitre>`.


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

- |Feature| Added select K best features functionality to
  :class:`feature_selection.SelectFromModel`.
  :issue:`6689` by :user:`Nihar Sheth <nsheth12>` and
  :user:`Quazi Rahman <qmaruf>`.

- |Feature| Added ``min_features_to_select`` parameter to
  :class:`feature_selection.RFECV` to bound evaluated features counts.
  :issue:`11293` by :user:`Brent Yi <brentyi>`.

- |Feature| :class:`feature_selection.RFECV`'s fit method now supports
  :term:`groups`.  :issue:`9656` by :user:`Adam Greenhall <adamgreenhall>`.

- |Fix| Fixed computation of ``n_features_to_compute`` for edge case with tied
  CV scores in :class:`feature_selection.RFECV`.
  :issue:`9222` by :user:`Nick Hoh <nickypie>`.

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

- |Efficiency| In :class:`gaussian_process.GaussianProcessRegressor`, method
  ``predict`` is faster when using ``return_std=True`` in particular more when
  called several times in a row. :issue:`9234` by :user:`andrewww <andrewww>`
  and :user:`Minghui Liu <minghui-liu>`.


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

- New module, adopting ``preprocessing.Imputer`` as
  :class:`impute.SimpleImputer` with minor changes (see under preprocessing
  below).

- |MajorFeature| Added :class:`impute.MissingIndicator` which generates a
  binary indicator for missing values. :issue:`8075` by :user:`Maniteja Nandana
  <maniteja123>` and :user:`Guillaume Lemaitre <glemaitre>`.

- |Feature| The :class:`impute.SimpleImputer` has a new strategy,
  ``'constant'``, to complete missing values with a fixed one, given by the
  ``fill_value`` parameter. This strategy supports numeric and non-numeric
  data, and so does the ``'most_frequent'`` strategy now. :issue:`11211` by
  :user:`Jeremie du Boisberranger <jeremiedbb>`.


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

- |Fix| Fixed a bug in :class:`isotonic.IsotonicRegression` which incorrectly
  combined weights when fitting a model to data involving points with
  identical X values.
  :issue:`9484` by :user:`Dallas Card <dallascard>`


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

- |Feature| :class:`linear_model.SGDClassifier`,
  :class:`linear_model.SGDRegressor`,
  :class:`linear_model.PassiveAggressiveClassifier`,
  :class:`linear_model.PassiveAggressiveRegressor` and
  :class:`linear_model.Perceptron` now expose ``early_stopping``,
  ``validation_fraction`` and ``n_iter_no_change`` parameters, to stop
  optimization monitoring the score on a validation set. A new learning rate
  ``"adaptive"`` strategy divides the learning rate by 5 each time
  ``n_iter_no_change`` consecutive epochs fail to improve the model.
  :issue:`9043` by `Tom Dupre la Tour`_.

- |Feature| Add `sample_weight` parameter to the fit method of
  :class:`linear_model.BayesianRidge` for weighted linear regression.
  :issue:`10112` by :user:`Peter St. John <pstjohn>`.

- |Fix| Fixed a bug in `logistic.logistic_regression_path` to ensure
  that the returned coefficients are correct when ``multiclass='multinomial'``.
  Previously, some of the coefficients would override each other, leading to
  incorrect results in :class:`linear_model.LogisticRegressionCV`.
  :issue:`11724` by :user:`Nicolas Hug <NicolasHug>`.

- |Fix| Fixed a bug in :class:`linear_model.LogisticRegression` where when using
  the parameter ``multi_class='multinomial'``, the ``predict_proba`` method was
  returning incorrect probabilities in the case of binary outcomes.
  :issue:`9939` by :user:`Roger Westover <rwolst>`.

- |Fix| Fixed a bug in :class:`linear_model.LogisticRegressionCV` where the
  ``score`` method always computes accuracy, not the metric given by
  the ``scoring`` parameter.
  :issue:`10998` by :user:`Thomas Fan <thomasjpfan>`.

- |Fix| Fixed a bug in :class:`linear_model.LogisticRegressionCV` where the
  'ovr' strategy was always used to compute cross-validation scores in the
  multiclass setting, even if ``'multinomial'`` was set.
  :issue:`8720` by :user:`William de Vazelhes <wdevazelhes>`.

- |Fix| Fixed a bug in :class:`linear_model.OrthogonalMatchingPursuit` that was
  broken when setting ``normalize=False``.
  :issue:`10071` by `Alexandre Gramfort`_.

- |Fix| Fixed a bug in :class:`linear_model.ARDRegression` which caused
  incorrectly updated estimates for the standard deviation and the
  coefficients. :issue:`10153` by :user:`Jörg Döpfert <jdoepfert>`.

- |Fix| Fixed a bug in :class:`linear_model.ARDRegression` and
  :class:`linear_model.BayesianRidge` which caused NaN predictions when fitted
  with a constant target.
  :issue:`10095` by :user:`Jörg Döpfert <jdoepfert>`.

- |Fix| Fixed a bug in :class:`linear_model.RidgeClassifierCV` where
  the parameter ``store_cv_values`` was not implemented though
  it was documented in ``cv_values`` as a way to set up the storage
  of cross-validation values for different alphas. :issue:`10297` by
  :user:`Mabel Villalba-Jiménez <mabelvj>`.

- |Fix| Fixed a bug in :class:`linear_model.ElasticNet` which caused the input
  to be overridden when using parameter ``copy_X=True`` and
  ``check_input=False``. :issue:`10581` by :user:`Yacine Mazari <ymazari>`.

- |Fix| Fixed a bug in :class:`sklearn.linear_model.Lasso`
  where the coefficient had wrong shape when ``fit_intercept=False``.
  :issue:`10687` by :user:`Martin Hahn <martin-hahn>`.

- |Fix| Fixed a bug in :func:`sklearn.linear_model.LogisticRegression` where the
  ``multi_class='multinomial'`` with binary output ``with warm_start=True``
  :issue:`10836` by :user:`Aishwarya Srinivasan <aishgrt1>`.

- |Fix| Fixed a bug in :class:`linear_model.RidgeCV` where using integer
  ``alphas`` raised an error.
  :issue:`10397` by :user:`Mabel Villalba-Jiménez <mabelvj>`.

- |Fix| Fixed condition triggering gap computation in
  :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet` when working
  with sparse matrices. :issue:`10992` by `Alexandre Gramfort`_.

- |Fix| Fixed a bug in :class:`linear_model.SGDClassifier`,
  :class:`linear_model.SGDRegressor`,
  :class:`linear_model.PassiveAggressiveClassifier`,
  :class:`linear_model.PassiveAggressiveRegressor` and
  :class:`linear_model.Perceptron`, where the stopping criterion was stopping
  the algorithm before convergence. A parameter ``n_iter_no_change`` was added
  and set by default to 5. Previous behavior is equivalent to setting the
  parameter to 1. :issue:`9043` by `Tom Dupre la Tour`_.

- |Fix| Fixed a bug where liblinear and libsvm-based estimators would segfault
  if passed a scipy.sparse matrix with 64-bit indices. They now raise a
  ValueError.
  :issue:`11327` by :user:`Karan Dhingra <kdhingra307>` and `Joel Nothman`_.

- |API| The default values of the ``solver`` and ``multi_class`` parameters of
  :class:`linear_model.LogisticRegression` will change respectively from
  ``'liblinear'`` and ``'ovr'`` in version 0.20 to ``'lbfgs'`` and
  ``'auto'`` in version 0.22. A FutureWarning is raised when the default
  values are used. :issue:`11905` by `Tom Dupre la Tour`_ and `Joel Nothman`_.

- |API| Deprecate ``positive=True`` option in :class:`linear_model.Lars` as
  the underlying implementation is broken. Use :class:`linear_model.Lasso`
  instead. :issue:`9837` by `Alexandre Gramfort`_.

- |API| ``n_iter_`` may vary from previous releases in
  :class:`linear_model.LogisticRegression` with ``solver='lbfgs'`` and
  :class:`linear_model.HuberRegressor`. For Scipy <= 1.0.0, the optimizer could
  perform more than the requested maximum number of iterations. Now both
  estimators will report at most ``max_iter`` iterations even if more were
  performed. :issue:`10723` by `Joel Nothman`_.


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

- |Efficiency| Speed improvements for both 'exact' and 'barnes_hut' methods in
  :class:`manifold.TSNE`. :issue:`10593` and :issue:`10610` by
  `Tom Dupre la Tour`_.

- |Feature| Support sparse input in :meth:`manifold.Isomap.fit`.
  :issue:`8554` by :user:`Leland McInnes <lmcinnes>`.

- |Feature| `manifold.t_sne.trustworthiness` accepts metrics other than
  Euclidean. :issue:`9775` by :user:`William de Vazelhes <wdevazelhes>`.

- |Fix| Fixed a bug in :func:`manifold.spectral_embedding` where the
  normalization of the spectrum was using a division instead of a
  multiplication. :issue:`8129` by :user:`Jan Margeta <jmargeta>`,
  :user:`Guillaume Lemaitre <glemaitre>`, and :user:`Devansh D.
  <devanshdalal>`.

- |API| |Feature| Deprecate ``precomputed`` parameter in function
  `manifold.t_sne.trustworthiness`. Instead, the new parameter ``metric``
  should be used with any compatible metric including 'precomputed', in which
  case the input matrix ``X`` should be a matrix of pairwise distances or
  squared distances. :issue:`9775` by :user:`William de Vazelhes
  <wdevazelhes>`.

- |API| Deprecate ``precomputed`` parameter in function
  `manifold.t_sne.trustworthiness`. Instead, the new parameter
  ``metric`` should be used with any compatible metric including
  'precomputed', in which case the input matrix ``X`` should be a matrix of
  pairwise distances or squared distances. :issue:`9775` by
  :user:`William de Vazelhes <wdevazelhes>`.


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

- |MajorFeature| Added the :func:`metrics.davies_bouldin_score` metric for
  evaluation of clustering models without a ground truth. :issue:`10827` by
  :user:`Luis Osa <logc>`.

- |MajorFeature| Added the :func:`metrics.balanced_accuracy_score` metric and
  a corresponding ``'balanced_accuracy'`` scorer for binary and multiclass
  classification. :issue:`8066` by :user:`xyguo` and :user:`Aman Dalmia
  <dalmia>`, and :issue:`10587` by `Joel Nothman`_.

- |Feature| Partial AUC is available via ``max_fpr`` parameter in
  :func:`metrics.roc_auc_score`. :issue:`3840` by
  :user:`Alexander Niederbühl <Alexander-N>`.

- |Feature| A scorer based on :func:`metrics.brier_score_loss` is also
  available. :issue:`9521` by :user:`Hanmin Qin <qinhanmin2014>`.

- |Feature| Added control over the normalization in
  :func:`metrics.normalized_mutual_info_score` and
  :func:`metrics.adjusted_mutual_info_score` via the ``average_method``
  parameter. In version 0.22, the default normalizer for each will become
  the *arithmetic* mean of the entropies of each clustering. :issue:`11124` by
  :user:`Arya McCarthy <aryamccarthy>`.

- |Feature| Added ``output_dict`` parameter in :func:`metrics.classification_report`
  to return classification statistics as dictionary.
  :issue:`11160` by :user:`Dan Barkhorn <danielbarkhorn>`.

- |Feature| :func:`metrics.classification_report` now reports all applicable averages on
  the given data, including micro, macro and weighted average as well as samples
  average for multilabel data. :issue:`11679` by :user:`Alexander Pacha <apacha>`.

- |Feature| :func:`metrics.average_precision_score` now supports binary
  ``y_true`` other than ``{0, 1}`` or ``{-1, 1}`` through ``pos_label``
  parameter. :issue:`9980` by :user:`Hanmin Qin <qinhanmin2014>`.

- |Feature| :func:`metrics.label_ranking_average_precision_score` now supports
  ``sample_weight``.
  :issue:`10845` by :user:`Jose Perez-Parras Toledano <jopepato>`.

- |Feature| Add ``dense_output`` parameter to :func:`metrics.pairwise.linear_kernel`.
  When False and both inputs are sparse, will return a sparse matrix.
  :issue:`10999` by :user:`Taylor G Smith <tgsmith61591>`.

- |Efficiency| :func:`metrics.silhouette_score` and
  :func:`metrics.silhouette_samples` are more memory efficient and run
  faster. This avoids some reported freezes and MemoryErrors.
  :issue:`11135` by `Joel Nothman`_.

- |Fix| Fixed a bug in :func:`metrics.precision_recall_fscore_support`
  when truncated `range(n_labels)` is passed as value for `labels`.
  :issue:`10377` by :user:`Gaurav Dhingra <gxyd>`.

- |Fix| Fixed a bug due to floating point error in
  :func:`metrics.roc_auc_score` with non-integer sample weights. :issue:`9786`
  by :user:`Hanmin Qin <qinhanmin2014>`.

- |Fix| Fixed a bug where :func:`metrics.roc_curve` sometimes starts on y-axis
  instead of (0, 0), which is inconsistent with the document and other
  implementations. Note that this will not influence the result from
  :func:`metrics.roc_auc_score` :issue:`10093` by :user:`alexryndin
  <alexryndin>` and :user:`Hanmin Qin <qinhanmin2014>`.

- |Fix| Fixed a bug to avoid integer overflow. Casted product to 64 bits integer in
  :func:`metrics.mutual_info_score`.
  :issue:`9772` by :user:`Kumar Ashutosh <thechargedneutron>`.

- |Fix| Fixed a bug where :func:`metrics.average_precision_score` will sometimes return
  ``nan`` when ``sample_weight`` contains 0.
  :issue:`9980` by :user:`Hanmin Qin <qinhanmin2014>`.

- |Fix| Fixed a bug in :func:`metrics.fowlkes_mallows_score` to avoid integer
  overflow. Casted return value of `contingency_matrix` to `int64` and computed
  product of square roots rather than square root of product.
  :issue:`9515` by :user:`Alan Liddell <aliddell>` and
  :user:`Manh Dao <manhdao>`.

- |API| Deprecate ``reorder`` parameter in :func:`metrics.auc` as it's no
  longer required for :func:`metrics.roc_auc_score`. Moreover using
  ``reorder=True`` can hide bugs due to floating point error in the input.
  :issue:`9851` by :user:`Hanmin Qin <qinhanmin2014>`.

- |API| In :func:`metrics.normalized_mutual_info_score` and
  :func:`metrics.adjusted_mutual_info_score`, warn that
  ``average_method`` will have a new default value. In version 0.22, the
  default normalizer for each will become the *arithmetic* mean of the
  entropies of each clustering. Currently,
  :func:`metrics.normalized_mutual_info_score` uses the default of
  ``average_method='geometric'``, and
  :func:`metrics.adjusted_mutual_info_score` uses the default of
  ``average_method='max'`` to match their behaviors in version 0.19.
  :issue:`11124` by :user:`Arya McCarthy <aryamccarthy>`.

- |API| The ``batch_size`` parameter to :func:`metrics.pairwise_distances_argmin_min`
  and :func:`metrics.pairwise_distances_argmin` is deprecated to be removed in
  v0.22. It no longer has any effect, as batch size is determined by global
  ``working_memory`` config. See :ref:`working_memory`. :issue:`10280` by `Joel
  Nothman`_ and :user:`Aman Dalmia <dalmia>`.


:mod:`sklearn.mixture`
......................

- |Feature| Added function :term:`fit_predict` to :class:`mixture.GaussianMixture`
  and :class:`mixture.GaussianMixture`, which is essentially equivalent to
  calling :term:`fit` and :term:`predict`. :issue:`10336` by :user:`Shu Haoran
  <haoranShu>` and :user:`Andrew Peng <Andrew-peng>`.

- |Fix| Fixed a bug in `mixture.BaseMixture` where the reported `n_iter_` was
  missing an iteration. It affected :class:`mixture.GaussianMixture` and
  :class:`mixture.BayesianGaussianMixture`. :issue:`10740` by :user:`Erich
  Schubert <kno10>` and :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| Fixed a bug in `mixture.BaseMixture` and its subclasses
  :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`
  where the ``lower_bound_`` was not the max lower bound across all
  initializations (when ``n_init > 1``), but just the lower bound of the last
  initialization. :issue:`10869` by :user:`Aurélien Géron <ageron>`.


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

- |Feature| Add `return_estimator` parameter in
  :func:`model_selection.cross_validate` to return estimators fitted on each
  split. :issue:`9686` by :user:`Aurélien Bellet <bellet>`.

- |Feature| New ``refit_time_`` attribute will be stored in
  :class:`model_selection.GridSearchCV` and
  :class:`model_selection.RandomizedSearchCV` if ``refit`` is set to ``True``.
  This will allow measuring the complete time it takes to perform
  hyperparameter optimization and refitting the best model on the whole
  dataset. :issue:`11310` by :user:`Matthias Feurer <mfeurer>`.

- |Feature| Expose `error_score` parameter in
  :func:`model_selection.cross_validate`,
  :func:`model_selection.cross_val_score`,
  :func:`model_selection.learning_curve` and
  :func:`model_selection.validation_curve` to control the behavior triggered
  when an error occurs in `model_selection._fit_and_score`.
  :issue:`11576` by :user:`Samuel O. Ronsin <samronsin>`.

- |Feature| `BaseSearchCV` now has an experimental, private interface to
  support customized parameter search strategies, through its ``_run_search``
  method. See the implementations in :class:`model_selection.GridSearchCV` and
  :class:`model_selection.RandomizedSearchCV` and please provide feedback if
  you use this. Note that we do not assure the stability of this API beyond
  version 0.20. :issue:`9599` by `Joel Nothman`_

- |Enhancement| Add improved error message in
  :func:`model_selection.cross_val_score` when multiple metrics are passed in
  ``scoring`` keyword. :issue:`11006` by :user:`Ming Li <minggli>`.

- |API| The default number of cross-validation folds ``cv`` and the default
  number of splits ``n_splits`` in the :class:`model_selection.KFold`-like
  splitters will change from 3 to 5 in 0.22 as 3-fold has a lot of variance.
  :issue:`11557` by :user:`Alexandre Boucaud <aboucaud>`.

- |API| The default of ``iid`` parameter of :class:`model_selection.GridSearchCV`
  and :class:`model_selection.RandomizedSearchCV` will change from ``True`` to
  ``False`` in version 0.22 to correspond to the standard definition of
  cross-validation, and the parameter will be removed in version 0.24
  altogether. This parameter is of greatest practical significance where the
  sizes of different test sets in cross-validation were very unequal, i.e. in
  group-based CV strategies. :issue:`9085` by :user:`Laurent Direr <ldirer>`
  and `Andreas Müller`_.

- |API| The default value of the ``error_score`` parameter in
  :class:`model_selection.GridSearchCV` and
  :class:`model_selection.RandomizedSearchCV` will change to ``np.NaN`` in
  version 0.22. :issue:`10677` by :user:`Kirill Zhdanovich <Zhdanovich>`.

- |API| Changed ValueError exception raised in
  :class:`model_selection.ParameterSampler` to a UserWarning for case where the
  class is instantiated with a greater value of ``n_iter`` than the total space
  of parameters in the parameter grid. ``n_iter`` now acts as an upper bound on
  iterations. :issue:`10982` by :user:`Juliet Lawton <julietcl>`

- |API| Invalid input for :class:`model_selection.ParameterGrid` now
  raises TypeError.
  :issue:`10928` by :user:`Solutus Immensus <solutusimmensus>`


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

- |MajorFeature| Added :class:`multioutput.RegressorChain` for multi-target
  regression. :issue:`9257` by :user:`Kumar Ashutosh <thechargedneutron>`.


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

- |MajorFeature| Added :class:`naive_bayes.ComplementNB`, which implements the
  Complement Naive Bayes classifier described in Rennie et al. (2003).
  :issue:`8190` by :user:`Michael A. Alcorn <airalcorn2>`.

- |Feature| Add `var_smoothing` parameter in :class:`naive_bayes.GaussianNB`
  to give a precise control over variances calculation.
  :issue:`9681` by :user:`Dmitry Mottl <Mottl>`.

- |Fix| Fixed a bug in :class:`naive_bayes.GaussianNB` which incorrectly
  raised error for prior list which summed to 1.
  :issue:`10005` by :user:`Gaurav Dhingra <gxyd>`.

- |Fix| Fixed a bug in :class:`naive_bayes.MultinomialNB` which did not accept
  vector valued pseudocounts (alpha).
  :issue:`10346` by :user:`Tobias Madsen <TobiasMadsen>`


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

- |Efficiency| :class:`neighbors.RadiusNeighborsRegressor` and
  :class:`neighbors.RadiusNeighborsClassifier` are now
  parallelized according to ``n_jobs`` regardless of ``algorithm``.
  :issue:`10887` by :user:`Joël Billaud <recamshak>`.

- |Efficiency| :mod:`sklearn.neighbors` query methods are now more
  memory efficient when ``algorithm='brute'``.
  :issue:`11136` by `Joel Nothman`_ and :user:`Aman Dalmia <dalmia>`.

- |Feature| Add ``sample_weight`` parameter to the fit method of
  :class:`neighbors.KernelDensity` to enable weighting in kernel density
  estimation.
  :issue:`4394` by :user:`Samuel O. Ronsin <samronsin>`.

- |Feature| Novelty detection with :class:`neighbors.LocalOutlierFactor`:
  Add a ``novelty`` parameter to :class:`neighbors.LocalOutlierFactor`. When
  ``novelty`` is set to True, :class:`neighbors.LocalOutlierFactor` can then
  be used for novelty detection, i.e. predict on new unseen data. Available
  prediction methods are ``predict``, ``decision_function`` and
  ``score_samples``. By default, ``novelty`` is set to ``False``, and only
  the ``fit_predict`` method is available.
  By :user:`Albert Thomas <albertcthomas>`.

- |Fix| Fixed a bug in :class:`neighbors.NearestNeighbors` where fitting a
  NearestNeighbors model fails when a) the distance metric used is a
  callable and b) the input to the NearestNeighbors model is sparse.
  :issue:`9579` by :user:`Thomas Kober <tttthomasssss>`.

- |Fix| Fixed a bug so ``predict`` in
  :class:`neighbors.RadiusNeighborsRegressor` can handle empty neighbor set
  when using non uniform weights. Also raises a new warning when no neighbors
  are found for samples. :issue:`9655` by :user:`Andreas Bjerre-Nielsen
  <abjer>`.

- |Fix| |Efficiency| Fixed a bug in ``KDTree`` construction that results in
  faster construction and querying times.
  :issue:`11556` by :user:`Jake VanderPlas <jakevdp>`

- |Fix| Fixed a bug in :class:`neighbors.KDTree` and :class:`neighbors.BallTree` where
  pickled tree objects would change their type to the super class `BinaryTree`.
  :issue:`11774` by :user:`Nicolas Hug <NicolasHug>`.


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

- |Feature| Add `n_iter_no_change` parameter in
  `neural_network.BaseMultilayerPerceptron`,
  :class:`neural_network.MLPRegressor`, and
  :class:`neural_network.MLPClassifier` to give control over
  maximum number of epochs to not meet ``tol`` improvement.
  :issue:`9456` by :user:`Nicholas Nadeau <nnadeau>`.

- |Fix| Fixed a bug in `neural_network.BaseMultilayerPerceptron`,
  :class:`neural_network.MLPRegressor`, and
  :class:`neural_network.MLPClassifier` with new ``n_iter_no_change``
  parameter now at 10 from previously hardcoded 2.
  :issue:`9456` by :user:`Nicholas Nadeau <nnadeau>`.

- |Fix| Fixed a bug in :class:`neural_network.MLPRegressor` where fitting
  quit unexpectedly early due to local minima or fluctuations.
  :issue:`9456` by :user:`Nicholas Nadeau <nnadeau>`


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

- |Feature| The ``predict`` method of :class:`pipeline.Pipeline` now passes
  keyword arguments on to the pipeline's last estimator, enabling the use of
  parameters such as ``return_std`` in a pipeline with caution.
  :issue:`9304` by :user:`Breno Freitas <brenolf>`.

- |API| :class:`pipeline.FeatureUnion` now supports ``'drop'`` as a transformer
  to drop features. :issue:`11144` by :user:`Thomas Fan <thomasjpfan>`.


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

- |MajorFeature| Expanded :class:`preprocessing.OneHotEncoder` to allow to
  encode categorical string features as a numeric array using a one-hot (or
  dummy) encoding scheme, and added :class:`preprocessing.OrdinalEncoder` to
  convert to ordinal integers. Those two classes now handle encoding of all
  feature types (also handles string-valued features) and derives the
  categories based on the unique values in the features instead of the maximum
  value in the features. :issue:`9151` and :issue:`10521` by :user:`Vighnesh
  Birodkar <vighneshbirodkar>` and `Joris Van den Bossche`_.

- |MajorFeature| Added :class:`preprocessing.KBinsDiscretizer` for turning
  continuous features into categorical or one-hot encoded
  features. :issue:`7668`, :issue:`9647`, :issue:`10195`,
  :issue:`10192`, :issue:`11272`, :issue:`11467` and :issue:`11505`.
  by :user:`Henry Lin <hlin117>`, `Hanmin Qin`_,
  `Tom Dupre la Tour`_ and :user:`Giovanni Giuseppe Costa <ggc87>`.

- |MajorFeature| Added :class:`preprocessing.PowerTransformer`, which
  implements the Yeo-Johnson and Box-Cox power transformations. Power
  transformations try to find a set of feature-wise parametric transformations
  to approximately map data to a Gaussian distribution centered at zero and
  with unit variance. This is useful as a variance-stabilizing transformation
  in situations where normality and homoscedasticity are desirable.
  :issue:`10210` by :user:`Eric Chang <chang>` and :user:`Maniteja
  Nandana <maniteja123>`, and :issue:`11520` by :user:`Nicolas Hug
  <nicolashug>`.

- |MajorFeature| NaN values are ignored and handled in the following
  preprocessing methods:
  :class:`preprocessing.MaxAbsScaler`,
  :class:`preprocessing.MinMaxScaler`,
  :class:`preprocessing.RobustScaler`,
  :class:`preprocessing.StandardScaler`,
  :class:`preprocessing.PowerTransformer`,
  :class:`preprocessing.QuantileTransformer` classes and
  :func:`preprocessing.maxabs_scale`,
  :func:`preprocessing.minmax_scale`,
  :func:`preprocessing.robust_scale`,
  :func:`preprocessing.scale`,
  :func:`preprocessing.power_transform`,
  :func:`preprocessing.quantile_transform` functions respectively addressed in
  issues :issue:`11011`, :issue:`11005`, :issue:`11308`, :issue:`11206`,
  :issue:`11306`, and :issue:`10437`.
  By :user:`Lucija Gregov <LucijaGregov>` and
  :user:`Guillaume Lemaitre <glemaitre>`.

- |Feature| :class:`preprocessing.PolynomialFeatures` now supports sparse
  input. :issue:`10452` by :user:`Aman Dalmia <dalmia>` and `Joel Nothman`_.

- |Feature| :class:`preprocessing.RobustScaler` and
  :func:`preprocessing.robust_scale` can be fitted using sparse matrices.
  :issue:`11308` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Feature| :class:`preprocessing.OneHotEncoder` now supports the
  `get_feature_names` method to obtain the transformed feature names.
  :issue:`10181` by :user:`Nirvan Anjirbag <Nirvan101>` and
  `Joris Van den Bossche`_.

- |Feature| A parameter ``check_inverse`` was added to
  :class:`preprocessing.FunctionTransformer` to ensure that ``func`` and
  ``inverse_func`` are the inverse of each other.
  :issue:`9399` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Feature| The ``transform`` method of :class:`sklearn.preprocessing.MultiLabelBinarizer`
  now ignores any unknown classes. A warning is raised stating the unknown classes
  classes found which are ignored.
  :issue:`10913` by :user:`Rodrigo Agundez <rragundez>`.

- |Fix| Fixed bugs in :class:`preprocessing.LabelEncoder` which would
  sometimes throw errors when ``transform`` or ``inverse_transform`` was called
  with empty arrays. :issue:`10458` by :user:`Mayur Kulkarni <maykulkarni>`.

- |Fix| Fix ValueError in :class:`preprocessing.LabelEncoder` when using
  ``inverse_transform`` on unseen labels. :issue:`9816` by :user:`Charlie Newey
  <newey01c>`.

- |Fix| Fix bug in :class:`preprocessing.OneHotEncoder` which discarded the
  ``dtype`` when returning a sparse matrix output.
  :issue:`11042` by :user:`Daniel Morales <DanielMorales9>`.

- |Fix| Fix ``fit`` and ``partial_fit`` in
  :class:`preprocessing.StandardScaler` in the rare case when ``with_mean=False``
  and `with_std=False` which was crashing by calling ``fit`` more than once and
  giving inconsistent results for ``mean_`` whether the input was a sparse or a
  dense matrix. ``mean_`` will be set to ``None`` with both sparse and dense
  inputs. ``n_samples_seen_`` will be also reported for both input types.
  :issue:`11235` by :user:`Guillaume Lemaitre <glemaitre>`.

- |API| Deprecate ``n_values`` and ``categorical_features`` parameters and
  ``active_features_``, ``feature_indices_`` and ``n_values_`` attributes
  of :class:`preprocessing.OneHotEncoder`. The ``n_values`` parameter can be
  replaced with the new ``categories`` parameter, and the attributes with the
  new ``categories_`` attribute. Selecting the categorical features with
  the ``categorical_features`` parameter is now better supported using the
  :class:`compose.ColumnTransformer`.
  :issue:`10521` by `Joris Van den Bossche`_.

- |API| Deprecate `preprocessing.Imputer` and move
  the corresponding module to :class:`impute.SimpleImputer`.
  :issue:`9726` by :user:`Kumar Ashutosh
  <thechargedneutron>`.

- |API| The ``axis`` parameter that was in
  `preprocessing.Imputer` is no longer present in
  :class:`impute.SimpleImputer`. The behavior is equivalent
  to ``axis=0`` (impute along columns). Row-wise
  imputation can be performed with FunctionTransformer
  (e.g., ``FunctionTransformer(lambda X:
  SimpleImputer().fit_transform(X.T).T)``). :issue:`10829`
  by :user:`Guillaume Lemaitre <glemaitre>` and
  :user:`Gilberto Olimpio <gilbertoolimpio>`.

- |API| The NaN marker for the missing values has been changed
  between the :class:`preprocessing.Imputer` and the
  `impute.SimpleImputer`.
  ``missing_values='NaN'`` should now be
  ``missing_values=np.nan``. :issue:`11211` by
  :user:`Jeremie du Boisberranger <jeremiedbb>`.

- |API| In :class:`preprocessing.FunctionTransformer`, the default of
  ``validate`` will be from ``True`` to ``False`` in 0.22.
  :issue:`10655` by :user:`Guillaume Lemaitre <glemaitre>`.


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

- |Fix| Fixed a bug in :class:`svm.SVC` where when the argument ``kernel`` is
  unicode in Python2, the ``predict_proba`` method was raising an
  unexpected TypeError given dense inputs.
  :issue:`10412` by :user:`Jiongyan Zhang <qmick>`.

- |API| Deprecate ``random_state`` parameter in :class:`svm.OneClassSVM` as
  the underlying implementation is not random.
  :issue:`9497` by :user:`Albert Thomas <albertcthomas>`.

- |API| The default value of ``gamma`` parameter of :class:`svm.SVC`,
  :class:`~svm.NuSVC`, :class:`~svm.SVR`, :class:`~svm.NuSVR`,
  :class:`~svm.OneClassSVM` will change from ``'auto'`` to ``'scale'`` in
  version 0.22 to account better for unscaled features. :issue:`8361` by
  :user:`Gaurav Dhingra <gxyd>` and :user:`Ting Neo <neokt>`.


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

- |Enhancement| Although private (and hence not assured API stability),
  `tree._criterion.ClassificationCriterion` and
  `tree._criterion.RegressionCriterion` may now be cimported and
  extended. :issue:`10325` by :user:`Camil Staps <camilstaps>`.

- |Fix| Fixed a bug in `tree.BaseDecisionTree` with `splitter="best"`
  where split threshold could become infinite when values in X were
  near infinite. :issue:`10536` by :user:`Jonathan Ohayon <Johayon>`.

- |Fix| Fixed a bug in `tree.MAE` to ensure sample weights are being
  used during the calculation of tree MAE impurity. Previous behaviour could
  cause suboptimal splits to be chosen since the impurity calculation
  considered all samples to be of equal weight importance.
  :issue:`11464` by :user:`John Stott <JohnStott>`.


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

- |Feature| :func:`utils.check_array` and :func:`utils.check_X_y` now have
  ``accept_large_sparse`` to control whether scipy.sparse matrices with 64-bit
  indices should be rejected.
  :issue:`11327` by :user:`Karan Dhingra <kdhingra307>` and `Joel Nothman`_.

- |Efficiency| |Fix| Avoid copying the data in :func:`utils.check_array` when
  the input data is a memmap (and ``copy=False``). :issue:`10663` by
  :user:`Arthur Mensch <arthurmensch>` and :user:`Loïc Estève <lesteve>`.

- |API| :func:`utils.check_array` yield a ``FutureWarning`` indicating
  that arrays of bytes/strings will be interpreted as decimal numbers
  beginning in version 0.22. :issue:`10229` by :user:`Ryan Lee <rtlee9>`


Multiple modules
................

- |Feature| |API| More consistent outlier detection API:
  Add a ``score_samples`` method in :class:`svm.OneClassSVM`,
  :class:`ensemble.IsolationForest`, :class:`neighbors.LocalOutlierFactor`,
  :class:`covariance.EllipticEnvelope`. It allows to access raw score
  functions from original papers. A new ``offset_`` parameter allows to link
  ``score_samples`` and ``decision_function`` methods.
  The ``contamination`` parameter of :class:`ensemble.IsolationForest` and
  :class:`neighbors.LocalOutlierFactor` ``decision_function`` methods is used
  to define this ``offset_`` such that outliers (resp. inliers) have negative (resp.
  positive) ``decision_function`` values. By default, ``contamination`` is
  kept unchanged to 0.1 for a deprecation period. In 0.22, it will be set to "auto",
  thus using method-specific score offsets.
  In :class:`covariance.EllipticEnvelope` ``decision_function`` method, the
  ``raw_values`` parameter is deprecated as the shifted Mahalanobis distance
  will be always returned in 0.22. :issue:`9015` by `Nicolas Goix`_.

- |Feature| |API| A ``behaviour`` parameter has been introduced in :class:`ensemble.IsolationForest`
  to ensure backward compatibility.
  In the old behaviour, the ``decision_function`` is independent of the ``contamination``
  parameter. A threshold attribute depending on the ``contamination`` parameter is thus
  used.
  In the new behaviour the ``decision_function`` is dependent on the ``contamination``
  parameter, in such a way that 0 becomes its natural threshold to detect outliers.
  Setting behaviour to "old" is deprecated and will not be possible in version 0.22.
  Beside, the behaviour parameter will be removed in 0.24.
  :issue:`11553` by `Nicolas Goix`_.

- |API| Added convergence warning to :class:`svm.LinearSVC` and
  :class:`linear_model.LogisticRegression` when ``verbose`` is set to 0.
  :issue:`10881` by :user:`Alexandre Sevin <AlexandreSev>`.

- |API| Changed warning type from :class:`UserWarning` to
  :class:`exceptions.ConvergenceWarning` for failing convergence in
  `linear_model.logistic_regression_path`,
  :class:`linear_model.RANSACRegressor`, :func:`linear_model.ridge_regression`,
  :class:`gaussian_process.GaussianProcessRegressor`,
  :class:`gaussian_process.GaussianProcessClassifier`,
  :func:`decomposition.fastica`, :class:`cross_decomposition.PLSCanonical`,
  :class:`cluster.AffinityPropagation`, and :class:`cluster.Birch`.
  :issue:`10306` by :user:`Jonathan Siebert <jotasi>`.


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

- |MajorFeature| A new configuration parameter, ``working_memory`` was added
  to control memory consumption limits in chunked operations, such as the new
  :func:`metrics.pairwise_distances_chunked`. See :ref:`working_memory`.
  :issue:`10280` by `Joel Nothman`_ and :user:`Aman Dalmia <dalmia>`.

- |Feature| The version of :mod:`joblib` bundled with Scikit-learn is now 0.12.
  This uses a new default multiprocessing implementation, named `loky
  <https://github.com/tomMoral/loky>`_. While this may incur some memory and
  communication overhead, it should provide greater cross-platform stability
  than relying on Python standard library multiprocessing. :issue:`11741` by
  the Joblib developers, especially :user:`Thomas Moreau <tomMoral>` and
  `Olivier Grisel`_.

- |Feature| An environment variable to use the site joblib instead of the
  vendored one was added (:ref:`environment_variable`). The main API of joblib
  is now exposed in :mod:`sklearn.utils`.
  :issue:`11166` by `Gael Varoquaux`_.

- |Feature| Add almost complete PyPy 3 support. Known unsupported
  functionalities are :func:`datasets.load_svmlight_file`,
  :class:`feature_extraction.FeatureHasher` and
  :class:`feature_extraction.text.HashingVectorizer`. For running on PyPy,
  PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required.
  :issue:`11010` by :user:`Ronan Lamy <rlamy>` and `Roman Yurchak`_.

- |Feature| A utility method :func:`sklearn.show_versions()` was added to
  print out information relevant for debugging. It includes the user system,
  the Python executable, the version of the main libraries and BLAS binding
  information. :issue:`11596` by :user:`Alexandre Boucaud <aboucaud>`

- |Fix| Fixed a bug when setting parameters on meta-estimator, involving both
  a wrapped estimator and its parameter. :issue:`9999` by :user:`Marcus Voss
  <marcus-voss>` and `Joel Nothman`_.

- |Fix| Fixed a bug where calling :func:`sklearn.base.clone` was not thread
  safe and could result in a "pop from empty list" error. :issue:`9569`
  by `Andreas Müller`_.

- |API| The default value of ``n_jobs`` is changed from ``1`` to ``None`` in
  all related functions and classes. ``n_jobs=None`` means ``unset``. It will
  generally be interpreted as ``n_jobs=1``, unless the current
  ``joblib.Parallel`` backend context specifies otherwise (See
  :term:`Glossary <n_jobs>` for additional information). Note that this change
  happens immediately (i.e., without a deprecation cycle).
  :issue:`11741` by `Olivier Grisel`_.

- |Fix| Fixed a bug in validation helpers where passing a Dask DataFrame results
  in an error. :issue:`12462` by :user:`Zachariah Miller <zwmiller>`

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

These changes mostly affect library developers.

- Checks for transformers now apply if the estimator implements
  :term:`transform`, regardless of whether it inherits from
  :class:`sklearn.base.TransformerMixin`. :issue:`10474` by `Joel Nothman`_.

- Classifiers are now checked for consistency between :term:`decision_function`
  and categorical predictions.
  :issue:`10500` by :user:`Narine Kokhlikyan <NarineK>`.

- Allow tests in :func:`utils.estimator_checks.check_estimator` to test functions
  that accept pairwise data.
  :issue:`9701` by :user:`Kyle Johnson <gkjohns>`

- Allow :func:`utils.estimator_checks.check_estimator` to check that there is no
  private settings apart from parameters during estimator initialization.
  :issue:`9378` by :user:`Herilalaina Rakotoarison <herilalaina>`

- The set of checks in :func:`utils.estimator_checks.check_estimator` now includes a
  ``check_set_params`` test which checks that ``set_params`` is equivalent to
  passing parameters in ``__init__`` and warns if it encounters parameter
  validation. :issue:`7738` by :user:`Alvin Chiang <absolutelyNoWarranty>`

- Add invariance tests for clustering metrics. :issue:`8102` by :user:`Ankita
  Sinha <anki08>` and :user:`Guillaume Lemaitre <glemaitre>`.

- Add ``check_methods_subset_invariance`` to
  :func:`~utils.estimator_checks.check_estimator`, which checks that
  estimator methods are invariant if applied to a data subset.
  :issue:`10428` by :user:`Jonathan Ohayon <Johayon>`

- Add tests in :func:`utils.estimator_checks.check_estimator` to check that an
  estimator can handle read-only memmap input data. :issue:`10663` by
  :user:`Arthur Mensch <arthurmensch>` and :user:`Loïc Estève <lesteve>`.

- ``check_sample_weights_pandas_series`` now uses 8 rather than 6 samples
  to accommodate for the default number of clusters in :class:`cluster.KMeans`.
  :issue:`10933` by :user:`Johannes Hansen <jnhansen>`.

- Estimators are now checked for whether ``sample_weight=None`` equates to
  ``sample_weight=np.ones(...)``.
  :issue:`11558` by :user:`Sergul Aydore <sergulaydore>`.


Code and Documentation Contributors
-----------------------------------

Thanks to everyone who has contributed to the maintenance and improvement of the
project since version 0.19, including:

211217613, Aarshay Jain, absolutelyNoWarranty, Adam Greenhall, Adam Kleczewski,
Adam Richie-Halford, adelr, AdityaDaflapurkar, Adrin Jalali, Aidan Fitzgerald,
aishgrt1, Akash Shivram, Alan Liddell, Alan Yee, Albert Thomas, Alexander
Lenail, Alexander-N, Alexandre Boucaud, Alexandre Gramfort, Alexandre Sevin,
Alex Egg, Alvaro Perez-Diaz, Amanda, Aman Dalmia, Andreas Bjerre-Nielsen,
Andreas Mueller, Andrew Peng, Angus Williams, Aniruddha Dave, annaayzenshtat,
Anthony Gitter, Antonio Quinonez, Anubhav Marwaha, Arik Pamnani, Arthur Ozga,
Artiem K, Arunava, Arya McCarthy, Attractadore, Aurélien Bellet, Aurélien
Geron, Ayush Gupta, Balakumaran Manoharan, Bangda Sun, Barry Hart, Bastian
Venthur, Ben Lawson, Benn Roth, Breno Freitas, Brent Yi, brett koonce, Caio
Oliveira, Camil Staps, cclauss, Chady Kamar, Charlie Brummitt, Charlie Newey,
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