scikit-learn sklearn contributors rst releasenotes13 Version 1 3

Answer:
.. include:: _contributors.rst

.. currentmodule:: sklearn

.. _release_notes_1_3:

===========
Version 1.3
===========

For a short description of the main highlights of the release, please refer to
:ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_3_0.py`.

.. include:: changelog_legend.inc

.. _changes_1_3_2:

Version 1.3.2
=============

**October 2023**

Changelog
---------

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

- |Fix| All dataset fetchers now accept `data_home` as any object that implements
  the :class:`os.PathLike` interface, for instance, :class:`pathlib.Path`.
  :pr:`27468` by :user:`Yao Xiao <Charlie-XIAO>`.

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

- |Fix| Fixes a bug in :class:`decomposition.KernelPCA` by forcing the output of
  the internal :class:`preprocessing.KernelCenterer` to be a default array. When the
  arpack solver is used, it expects an array with a `dtype` attribute.
  :pr:`27583` by :user:`Guillaume Lemaitre <glemaitre>`.

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

- |Fix| Fixes a bug for metrics using `zero_division=np.nan`
  (e.g. :func:`~metrics.precision_score`) within a paralell loop
  (e.g. :func:`~model_selection.cross_val_score`) where the singleton for `np.nan`
  will be different in the sub-processes.
  :pr:`27573` by :user:`Guillaume Lemaitre <glemaitre>`.

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

- |Fix| Do not leak data via non-initialized memory in decision tree pickle files and make
  the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève <lesteve>`.

.. _changes_1_3_1:

Version 1.3.1
=============

**September 2023**

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.

- |Fix| Ridge models with `solver='sparse_cg'` may have slightly different
  results with scipy>=1.12, because of an underlying change in the scipy solver
  (see `scipy#18488 <https://github.com/scipy/scipy/pull/18488>`_ for more
  details)
  :pr:`26814` by :user:`Loïc Estève <lesteve>`

Changes impacting all modules
-----------------------------

- |Fix| The `set_output` API correctly works with list input. :pr:`27044` by
  `Thomas Fan`_.

Changelog
---------

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

- |Fix| :class:`calibration.CalibratedClassifierCV` can now handle models that
  produce large prediction scores. Before it was numerically unstable.
  :pr:`26913` by :user:`Omar Salman <OmarManzoor>`.

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

- |Fix| :class:`cluster.BisectingKMeans` could crash when predicting on data
  with a different scale than the data used to fit the model.
  :pr:`27167` by `Olivier Grisel`_.

- |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single feature.
  :pr:`27243` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

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

- |Fix| :class:`cross_decomposition.PLSRegression` now automatically ravels the output
  of `predict` if fitted with one dimensional `y`.
  :pr:`26602` by :user:`Yao Xiao <Charlie-XIAO>`.

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

- |Fix| Fix a bug in :class:`ensemble.AdaBoostClassifier` with `algorithm="SAMME"`
  where the decision function of each weak learner should be symmetric (i.e.
  the sum of the scores should sum to zero for a sample).
  :pr:`26521` by :user:`Guillaume Lemaitre <glemaitre>`.

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

- |Fix| :func:`feature_selection.mutual_info_regression` now correctly computes the
  result when `X` is of integer dtype. :pr:`26748` by :user:`Yao Xiao <Charlie-XIAO>`.

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

- |Fix| :class:`impute.KNNImputer` now correctly adds a missing indicator column in
  ``transform`` when ``add_indicator`` is set to ``True`` and missing values are observed
  during ``fit``. :pr:`26600` by :user:`Shreesha Kumar Bhat <Shreesha3112>`.

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

- |Fix| Scorers used with :func:`metrics.get_scorer` handle properly
  multilabel-indicator matrix.
  :pr:`27002` by :user:`Guillaume Lemaitre <glemaitre>`.

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

- |Fix| The initialization of :class:`mixture.GaussianMixture` from user-provided
  `precisions_init` for `covariance_type` of `full` or `tied` was not correct,
  and has been fixed.
  :pr:`26416` by :user:`Yang Tao <mchikyt3>`.

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

- |Fix| :meth:`neighbors.KNeighborsClassifier.predict` no longer raises an
  exception for `pandas.DataFrames` input.
  :pr:`26772` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| Reintroduce `sklearn.neighbors.BallTree.valid_metrics` and
  `sklearn.neighbors.KDTree.valid_metrics` as public class attributes.
  :pr:`26754` by :user:`Julien Jerphanion <jjerphan>`.

- |Fix| :class:`sklearn.model_selection.HalvingRandomSearchCV` no longer raises
  when the input to the `param_distributions` parameter is a list of dicts.
  :pr:`26893` by :user:`Stefanie Senger <StefanieSenger>`.

- |Fix| Neighbors based estimators now correctly work when `metric="minkowski"` and the
  metric parameter `p` is in the range `0 < p < 1`, regardless of the `dtype` of `X`.
  :pr:`26760` by :user:`Shreesha Kumar Bhat <Shreesha3112>`.

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

- |Fix| :class:`preprocessing.LabelEncoder` correctly accepts `y` as a keyword
  argument. :pr:`26940` by `Thomas Fan`_.

- |Fix| :class:`preprocessing.OneHotEncoder` shows a more informative error message
  when `sparse_output=True` and the output is configured to be pandas.
  :pr:`26931` by `Thomas Fan`_.

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

- |Fix| :func:`tree.plot_tree` now accepts `class_names=True` as documented.
  :pr:`26903` by :user:`Thomas Roehr <2maz>`

- |Fix| The `feature_names` parameter of :func:`tree.plot_tree` now accepts any kind of
  array-like instead of just a list. :pr:`27292` by :user:`Rahil Parikh <rprkh>`.

.. _changes_1_3:

Version 1.3.0
=============

**June 2023**

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.

- |Enhancement| :meth:`multiclass.OutputCodeClassifier.predict` now uses a more
  efficient pairwise distance reduction. As a consequence, the tie-breaking
  strategy is different and thus the predicted labels may be different.
  :pr:`25196` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Enhancement| The `fit_transform` method of :class:`decomposition.DictionaryLearning`
  is more efficient but may produce different results as in previous versions when
  `transform_algorithm` is not the same as `fit_algorithm` and the number of iterations
  is small. :pr:`24871` by :user:`Omar Salman <OmarManzoor>`.

- |Enhancement| The `sample_weight` parameter now will be used in centroids
  initialization for :class:`cluster.KMeans`, :class:`cluster.BisectingKMeans`
  and :class:`cluster.MiniBatchKMeans`.
  This change will break backward compatibility, since numbers generated
  from same random seeds will be different.
  :pr:`25752` by :user:`Gleb Levitski <glevv>`,
  :user:`Jérémie du Boisberranger <jeremiedbb>`,
  :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| Treat more consistently small values in the `W` and `H` matrices during the
  `fit` and `transform` steps of :class:`decomposition.NMF` and
  :class:`decomposition.MiniBatchNMF` which can produce different results than previous
  versions. :pr:`25438` by :user:`Yotam Avidar-Constantini <yotamcons>`.

- |Fix| :class:`decomposition.KernelPCA` may produce different results through
  `inverse_transform` if `gamma` is `None`. Now it will be chosen correctly as
  `1/n_features` of the data that it is fitted on, while previously it might be
  incorrectly chosen as `1/n_features` of the data passed to `inverse_transform`.
  A new attribute `gamma_` is provided for revealing the actual value of `gamma`
  used each time the kernel is called.
  :pr:`26337` by :user:`Yao Xiao <Charlie-XIAO>`.

Changed displays
----------------

- |Enhancement| :class:`model_selection.LearningCurveDisplay` displays both the
  train and test curves by default. You can set `score_type="test"` to keep the
  past behaviour.
  :pr:`25120` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| :class:`model_selection.ValidationCurveDisplay` now accepts passing a
  list to the `param_range` parameter.
  :pr:`27311` by :user:`Arturo Amor <ArturoAmorQ>`.

Changes impacting all modules
-----------------------------

- |Enhancement| The `get_feature_names_out` method of the following classes now
  raises a `NotFittedError` if the instance is not fitted. This ensures the error is
  consistent in all estimators with the `get_feature_names_out` method.

  - :class:`impute.MissingIndicator`
  - :class:`feature_extraction.DictVectorizer`
  - :class:`feature_extraction.text.TfidfTransformer`
  - :class:`feature_selection.GenericUnivariateSelect`
  - :class:`feature_selection.RFE`
  - :class:`feature_selection.RFECV`
  - :class:`feature_selection.SelectFdr`
  - :class:`feature_selection.SelectFpr`
  - :class:`feature_selection.SelectFromModel`
  - :class:`feature_selection.SelectFwe`
  - :class:`feature_selection.SelectKBest`
  - :class:`feature_selection.SelectPercentile`
  - :class:`feature_selection.SequentialFeatureSelector`
  - :class:`feature_selection.VarianceThreshold`
  - :class:`kernel_approximation.AdditiveChi2Sampler`
  - :class:`impute.IterativeImputer`
  - :class:`impute.KNNImputer`
  - :class:`impute.SimpleImputer`
  - :class:`isotonic.IsotonicRegression`
  - :class:`preprocessing.Binarizer`
  - :class:`preprocessing.KBinsDiscretizer`
  - :class:`preprocessing.MaxAbsScaler`
  - :class:`preprocessing.MinMaxScaler`
  - :class:`preprocessing.Normalizer`
  - :class:`preprocessing.OrdinalEncoder`
  - :class:`preprocessing.PowerTransformer`
  - :class:`preprocessing.QuantileTransformer`
  - :class:`preprocessing.RobustScaler`
  - :class:`preprocessing.SplineTransformer`
  - :class:`preprocessing.StandardScaler`
  - :class:`random_projection.GaussianRandomProjection`
  - :class:`random_projection.SparseRandomProjection`

  The `NotFittedError` displays an informative message asking to fit the instance
  with the appropriate arguments.

  :pr:`25294`, :pr:`25308`, :pr:`25291`, :pr:`25367`, :pr:`25402`,
  by :user:`John Pangas <jpangas>`, :user:`Rahil Parikh <rprkh>` ,
  and :user:`Alex Buzenet <albuzenet>`.

- |Enhancement| Added a multi-threaded Cython routine to the compute squared
  Euclidean distances (sometimes followed by a fused reduction operation) for a
  pair of datasets consisting of a sparse CSR matrix and a dense NumPy.

  This can improve the performance of following functions and estimators:

  - :func:`sklearn.metrics.pairwise_distances_argmin`
  - :func:`sklearn.metrics.pairwise_distances_argmin_min`
  - :class:`sklearn.cluster.AffinityPropagation`
  - :class:`sklearn.cluster.Birch`
  - :class:`sklearn.cluster.MeanShift`
  - :class:`sklearn.cluster.OPTICS`
  - :class:`sklearn.cluster.SpectralClustering`
  - :func:`sklearn.feature_selection.mutual_info_regression`
  - :class:`sklearn.neighbors.KNeighborsClassifier`
  - :class:`sklearn.neighbors.KNeighborsRegressor`
  - :class:`sklearn.neighbors.RadiusNeighborsClassifier`
  - :class:`sklearn.neighbors.RadiusNeighborsRegressor`
  - :class:`sklearn.neighbors.LocalOutlierFactor`
  - :class:`sklearn.neighbors.NearestNeighbors`
  - :class:`sklearn.manifold.Isomap`
  - :class:`sklearn.manifold.LocallyLinearEmbedding`
  - :class:`sklearn.manifold.TSNE`
  - :func:`sklearn.manifold.trustworthiness`
  - :class:`sklearn.semi_supervised.LabelPropagation`
  - :class:`sklearn.semi_supervised.LabelSpreading`

  A typical example of this performance improvement happens when passing a sparse
  CSR matrix to the `predict` or `transform` method of estimators that rely on
  a dense NumPy representation to store their fitted parameters (or the reverse).

  For instance, :meth:`sklearn.neighbors.NearestNeighbors.kneighbors` is now up
  to 2 times faster for this case on commonly available laptops.

  :pr:`25044` by :user:`Julien Jerphanion <jjerphan>`.

- |Enhancement| All estimators that internally rely on OpenMP multi-threading
  (via Cython) now use a number of threads equal to the number of physical
  (instead of logical) cores by default. In the past, we observed that using as
  many threads as logical cores on SMT hosts could sometimes cause severe
  performance problems depending on the algorithms and the shape of the data.
  Note that it is still possible to manually adjust the number of threads used
  by OpenMP as documented in :ref:`parallelism`.

  :pr:`26082` by :user:`Jérémie du Boisberranger <jeremiedbb>` and
  :user:`Olivier Grisel <ogrisel>`.

Experimental / Under Development
--------------------------------

- |MajorFeature| :ref:`Metadata routing <metadata_routing>`'s related base
  methods are included in this release. This feature is only available via the
  `enable_metadata_routing` feature flag which can be enabled using
  :func:`sklearn.set_config` and :func:`sklearn.config_context`. For now this
  feature is mostly useful for third party developers to prepare their code
  base for metadata routing, and we strongly recommend that they also hide it
  behind the same feature flag, rather than having it enabled by default.
  :pr:`24027` by `Adrin Jalali`_, :user:`Benjamin Bossan <BenjaminBossan>`, and
  :user:`Omar Salman <OmarManzoor>`.

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.

`sklearn`
.........

- |Feature| Added a new option `skip_parameter_validation`, to the function
  :func:`sklearn.set_config` and context manager :func:`sklearn.config_context`, that
  allows to skip the validation of the parameters passed to the estimators and public
  functions. This can be useful to speed up the code but should be used with care
  because it can lead to unexpected behaviors or raise obscure error messages when
  setting invalid parameters.
  :pr:`25815` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

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

- |Feature| A `__sklearn_clone__` protocol is now available to override the
  default behavior of :func:`base.clone`. :pr:`24568` by `Thomas Fan`_.

- |Fix| :class:`base.TransformerMixin` now currently keeps a namedtuple's class
  if `transform` returns a namedtuple. :pr:`26121` by `Thomas Fan`_.

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

- |Fix| :class:`calibration.CalibratedClassifierCV` now does not enforce sample
  alignment on `fit_params`. :pr:`25805` by `Adrin Jalali`_.

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

- |MajorFeature| Added :class:`cluster.HDBSCAN`, a modern hierarchical density-based
  clustering algorithm. Similarly to :class:`cluster.OPTICS`, it can be seen as a
  generalization of :class:`cluster.DBSCAN` by allowing for hierarchical instead of flat
  clustering, however it varies in its approach from :class:`cluster.OPTICS`. This
  algorithm is very robust with respect to its hyperparameters' values and can
  be used on a wide variety of data without much, if any, tuning.

  This implementation is an adaptation from the original implementation of HDBSCAN in
  `scikit-learn-contrib/hdbscan <https://github.com/scikit-learn-contrib/hdbscan>`_,
  by :user:`Leland McInnes <lmcinnes>` et al.

  :pr:`26385` by :user:`Meekail Zain <micky774>`

- |Enhancement| The `sample_weight` parameter now will be used in centroids
  initialization for :class:`cluster.KMeans`, :class:`cluster.BisectingKMeans`
  and :class:`cluster.MiniBatchKMeans`.
  This change will break backward compatibility, since numbers generated
  from same random seeds will be different.
  :pr:`25752` by :user:`Gleb Levitski <glevv>`,
  :user:`Jérémie du Boisberranger <jeremiedbb>`,
  :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| :class:`cluster.KMeans`, :class:`cluster.MiniBatchKMeans` and
  :func:`cluster.k_means` now correctly handle the combination of `n_init="auto"`
  and `init` being an array-like, running one initialization in that case.
  :pr:`26657` by :user:`Binesh Bannerjee <bnsh>`.

- |API| The `sample_weight` parameter in `predict` for
  :meth:`cluster.KMeans.predict` and :meth:`cluster.MiniBatchKMeans.predict`
  is now deprecated and will be removed in v1.5.
  :pr:`25251` by :user:`Gleb Levitski <glevv>`.

- |API| The `Xred` argument in :func:`cluster.FeatureAgglomeration.inverse_transform`
  is renamed to `Xt` and will be removed in v1.5. :pr:`26503` by `Adrin Jalali`_.

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

- |Fix| :class:`compose.ColumnTransformer` raises an informative error when the individual
  transformers of `ColumnTransformer` output pandas dataframes with indexes that are
  not consistent with each other and the output is configured to be pandas.
  :pr:`26286` by `Thomas Fan`_.

- |Fix| :class:`compose.ColumnTransformer` correctly sets the output of the
  remainder when `set_output` is called. :pr:`26323` by `Thomas Fan`_.

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

- |Fix| Allows `alpha=0` in :class:`covariance.GraphicalLasso` to be
  consistent with :func:`covariance.graphical_lasso`.
  :pr:`26033` by :user:`Genesis Valencia <genvalen>`.

- |Fix| :func:`covariance.empirical_covariance` now gives an informative
  error message when input is not appropriate.
  :pr:`26108` by :user:`Quentin Barthélemy <qbarthelemy>`.

- |API| Deprecates `cov_init` in :func:`covariance.graphical_lasso` in 1.3 since
  the parameter has no effect. It will be removed in 1.5.
  :pr:`26033` by :user:`Genesis Valencia <genvalen>`.

- |API| Adds `costs_` fitted attribute in :class:`covariance.GraphicalLasso` and
  :class:`covariance.GraphicalLassoCV`.
  :pr:`26033` by :user:`Genesis Valencia <genvalen>`.

- |API| Adds `covariance` parameter in :class:`covariance.GraphicalLasso`.
  :pr:`26033` by :user:`Genesis Valencia <genvalen>`.

- |API| Adds `eps` parameter in :class:`covariance.GraphicalLasso`,
  :func:`covariance.graphical_lasso`, and :class:`covariance.GraphicalLassoCV`.
  :pr:`26033` by :user:`Genesis Valencia <genvalen>`.

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

- |Enhancement| Allows to overwrite the parameters used to open the ARFF file using
  the parameter `read_csv_kwargs` in :func:`datasets.fetch_openml` when using the
  pandas parser.
  :pr:`26433` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| :func:`datasets.fetch_openml` returns improved data types when
  `as_frame=True` and `parser="liac-arff"`. :pr:`26386` by `Thomas Fan`_.

- |Fix| Following the ARFF specs, only the marker `"?"` is now considered as a missing
  values when opening ARFF files fetched using :func:`datasets.fetch_openml` when using
  the pandas parser. The parameter `read_csv_kwargs` allows to overwrite this behaviour.
  :pr:`26551` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| :func:`datasets.fetch_openml` will consistently use `np.nan` as missing marker
  with both parsers `"pandas"` and `"liac-arff"`.
  :pr:`26579` by :user:`Guillaume Lemaitre <glemaitre>`.

- |API| The `data_transposed` argument of :func:`datasets.make_sparse_coded_signal`
  is deprecated and will be removed in v1.5.
  :pr:`25784` by :user:`Jérémie du Boisberranger`.

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

- |Efficiency| :class:`decomposition.MiniBatchDictionaryLearning` and
  :class:`decomposition.MiniBatchSparsePCA` are now faster for small batch sizes by
  avoiding duplicate validations.
  :pr:`25490` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Enhancement| :class:`decomposition.DictionaryLearning` now accepts the parameter
  `callback` for consistency with the function :func:`decomposition.dict_learning`.
  :pr:`24871` by :user:`Omar Salman <OmarManzoor>`.

- |Fix| Treat more consistently small values in the `W` and `H` matrices during the
  `fit` and `transform` steps of :class:`decomposition.NMF` and
  :class:`decomposition.MiniBatchNMF` which can produce different results than previous
  versions. :pr:`25438` by :user:`Yotam Avidar-Constantini <yotamcons>`.

- |API| The `W` argument in :func:`decomposition.NMF.inverse_transform` and
  :class:`decomposition.MiniBatchNMF.inverse_transform` is renamed to `Xt` and
  will be removed in v1.5. :pr:`26503` by `Adrin Jalali`_.

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

- |Enhancement| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now
  supports the `PyTorch <https://pytorch.org/>`__. See
  :ref:`array_api` for more details. :pr:`25956` by `Thomas Fan`_.

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

- |Feature| :class:`ensemble.HistGradientBoostingRegressor` now supports
  the Gamma deviance loss via `loss="gamma"`.
  Using the Gamma deviance as loss function comes in handy for modelling skewed
  distributed, strictly positive valued targets.
  :pr:`22409` by :user:`Christian Lorentzen <lorentzenchr>`.

- |Feature| Compute a custom out-of-bag score by passing a callable to
  :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`,
  :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor`.
  :pr:`25177` by `Tim Head`_.

- |Feature| :class:`ensemble.GradientBoostingClassifier` now exposes
  out-of-bag scores via the `oob_scores_` or `oob_score_` attributes.
  :pr:`24882` by :user:`Ashwin Mathur <awinml>`.

- |Efficiency| :class:`ensemble.IsolationForest` predict time is now faster
  (typically by a factor of 8 or more). Internally, the estimator now precomputes
  decision path lengths per tree at `fit` time. It is therefore not possible
  to load an estimator trained with scikit-learn 1.2 to make it predict with
  scikit-learn 1.3: retraining with scikit-learn 1.3 is required.
  :pr:`25186` by :user:`Felipe Breve Siola <fsiola>`.

- |Efficiency| :class:`ensemble.RandomForestClassifier` and
  :class:`ensemble.RandomForestRegressor` with `warm_start=True` now only
  recomputes out-of-bag scores when there are actually more `n_estimators`
  in subsequent `fit` calls.
  :pr:`26318` by :user:`Joshua Choo Yun Keat <choo8>`.

- |Enhancement| :class:`ensemble.BaggingClassifier` and
  :class:`ensemble.BaggingRegressor` expose the `allow_nan` tag from the
  underlying estimator. :pr:`25506` by `Thomas Fan`_.

- |Fix| :meth:`ensemble.RandomForestClassifier.fit` sets `max_samples = 1`
  when `max_samples` is a float and `round(n_samples * max_samples) < 1`.
  :pr:`25601` by :user:`Jan Fidor <JanFidor>`.

- |Fix| :meth:`ensemble.IsolationForest.fit` no longer warns about missing
  feature names when called with `contamination` not `"auto"` on a pandas
  dataframe.
  :pr:`25931` by :user:`Yao Xiao <Charlie-XIAO>`.

- |Fix| :class:`ensemble.HistGradientBoostingRegressor` and
  :class:`ensemble.HistGradientBoostingClassifier` treats negative values for
  categorical features consistently as missing values, following LightGBM's and
  pandas' conventions.
  :pr:`25629` by `Thomas Fan`_.

- |Fix| Fix deprecation of `base_estimator` in :class:`ensemble.AdaBoostClassifier`
  and :class:`ensemble.AdaBoostRegressor` that was introduced in :pr:`23819`.
  :pr:`26242` by :user:`Marko Toplak <markotoplak>`.

:mod:`sklearn.exceptions`
.........................

- |Feature| Added :class:`exceptions.InconsistentVersionWarning` which is raised
  when a scikit-learn estimator is unpickled with a scikit-learn version that is
  inconsistent with the sckit-learn version the estimator was pickled with.
  :pr:`25297` by `Thomas Fan`_.

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

- |API| :class:`feature_extraction.image.PatchExtractor` now follows the
  transformer API of scikit-learn. This class is defined as a stateless transformer
  meaning that it is note required to call `fit` before calling `transform`.
  Parameter validation only happens at `fit` time.
  :pr:`24230` by :user:`Guillaume Lemaitre <glemaitre>`.

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

- |Enhancement| All selectors in :mod:`sklearn.feature_selection` will preserve
  a DataFrame's dtype when transformed. :pr:`25102` by `Thomas Fan`_.

- |Fix| :class:`feature_selection.SequentialFeatureSelector`'s `cv` parameter
  now supports generators. :pr:`25973` by `Yao Xiao <Charlie-XIAO>`.

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

- |Enhancement| Added the parameter `fill_value` to :class:`impute.IterativeImputer`.
  :pr:`25232` by :user:`Thijs van Weezel <ValueInvestorThijs>`.

- |Fix| :class:`impute.IterativeImputer` now correctly preserves the Pandas
  Index when the `set_config(transform_output="pandas")`. :pr:`26454` by `Thomas Fan`_.

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

- |Enhancement| Added support for `sample_weight` in
  :func:`inspection.partial_dependence` and
  :meth:`inspection.PartialDependenceDisplay.from_estimator`. This allows for
  weighted averaging when aggregating for each value of the grid we are making the
  inspection on. The option is only available when `method` is set to `brute`.
  :pr:`25209` and :pr:`26644` by :user:`Carlo Lemos <vitaliset>`.

- |API| :func:`inspection.partial_dependence` returns a :class:`utils.Bunch` with
  new key: `grid_values`. The `values` key is deprecated in favor of `grid_values`
  and the `values` key will be removed in 1.5.
  :pr:`21809` and :pr:`25732` by `Thomas Fan`_.

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

- |Fix| :class:`kernel_approximation.AdditiveChi2Sampler` is now stateless.
  The `sample_interval_` attribute is deprecated and will be removed in 1.5.
  :pr:`25190` by :user:`Vincent Maladière <Vincent-Maladiere>`.

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

- |Efficiency| Avoid data scaling when `sample_weight=None` and other
  unnecessary data copies and unexpected dense to sparse data conversion in
  :class:`linear_model.LinearRegression`.
  :pr:`26207` by :user:`Olivier Grisel <ogrisel>`.

- |Enhancement| :class:`linear_model.SGDClassifier`,
  :class:`linear_model.SGDRegressor` and :class:`linear_model.SGDOneClassSVM`
  now preserve dtype for `numpy.float32`.
  :pr:`25587` by :user:`Omar Salman <OmarManzoor>`.

- |Enhancement| The `n_iter_` attribute has been included in
  :class:`linear_model.ARDRegression` to expose the actual number of iterations
  required to reach the stopping criterion.
  :pr:`25697` by :user:`John Pangas <jpangas>`.

- |Fix| Use a more robust criterion to detect convergence of
  :class:`linear_model.LogisticRegression` with `penalty="l1"` and `solver="liblinear"`
  on linearly separable problems.
  :pr:`25214` by `Tom Dupre la Tour`_.

- |Fix| Fix a crash when calling `fit` on
  :class:`linear_model.LogisticRegression` with `solver="newton-cholesky"` and
  `max_iter=0` which failed to inspect the state of the model prior to the
  first parameter update.
  :pr:`26653` by :user:`Olivier Grisel <ogrisel>`.

- |API| Deprecates `n_iter` in favor of `max_iter` in
  :class:`linear_model.BayesianRidge` and :class:`linear_model.ARDRegression`.
  `n_iter` will be removed in scikit-learn 1.5. This change makes those
  estimators consistent with the rest of estimators.
  :pr:`25697` by :user:`John Pangas <jpangas>`.

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

- |Fix| :class:`manifold.Isomap` now correctly preserves the Pandas
  Index when the `set_config(transform_output="pandas")`. :pr:`26454` by `Thomas Fan`_.

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

- |Feature| Adds `zero_division=np.nan` 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`. When `zero_division=np.nan` and there is a
  zero division, the metric is undefined and is excluded from averaging. When not used
  for averages, the value returned is `np.nan`.
  :pr:`25531` by :user:`Marc Torrellas Socastro <marctorsoc>`.

- |Feature| :func:`metrics.average_precision_score` now supports the
  multiclass case.
  :pr:`17388` by :user:`Geoffrey Bolmier <gbolmier>` and
  :pr:`24769` by :user:`Ashwin Mathur <awinml>`.

- |Efficiency| The computation of the expected mutual information in
  :func:`metrics.adjusted_mutual_info_score` is now faster when the number of
  unique labels is large and its memory usage is reduced in general.
  :pr:`25713` by :user:`Kshitij Mathur <Kshitij68>`,
  :user:`Guillaume Lemaitre <glemaitre>`, :user:`Omar Salman <OmarManzoor>` and
  :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Enhancement| :class:`metrics.silhouette_samples` nows accepts a sparse
  matrix of pairwise distances between samples, or a feature array.
  :pr:`18723` by :user:`Sahil Gupta <sahilgupta2105>` and
  :pr:`24677` by :user:`Ashwin Mathur <awinml>`.

- |Enhancement| A new parameter `drop_intermediate` was added to
  :func:`metrics.precision_recall_curve`,
  :func:`metrics.PrecisionRecallDisplay.from_estimator`,
  :func:`metrics.PrecisionRecallDisplay.from_predictions`,
  which drops some suboptimal thresholds to create lighter precision-recall
  curves.
  :pr:`24668` by :user:`dberenbaum`.

- |Enhancement| :meth:`metrics.RocCurveDisplay.from_estimator` and
  :meth:`metrics.RocCurveDisplay.from_predictions` now accept two new keywords,
  `plot_chance_level` and `chance_level_kw` to plot the baseline chance
  level. This line is exposed in the `chance_level_` attribute.
  :pr:`25987` by :user:`Yao Xiao <Charlie-XIAO>`.

- |Enhancement| :meth:`metrics.PrecisionRecallDisplay.from_estimator` and
  :meth:`metrics.PrecisionRecallDisplay.from_predictions` now accept two new
  keywords, `plot_chance_level` and `chance_level_kw` to plot the baseline
  chance level. This line is exposed in the `chance_level_` attribute.
  :pr:`26019` by :user:`Yao Xiao <Charlie-XIAO>`.

- |Fix| :func:`metrics.pairwise.manhattan_distances` now supports readonly sparse datasets.
  :pr:`25432` by :user:`Julien Jerphanion <jjerphan>`.

- |Fix| Fixed :func:`metrics.classification_report` so that empty input will return
  `np.nan`. Previously, "macro avg" and `weighted avg` would return
  e.g. `f1-score=np.nan` and `f1-score=0.0`, being inconsistent. Now, they
  both return `np.nan`.
  :pr:`25531` by :user:`Marc Torrellas Socastro <marctorsoc>`.

- |Fix| :func:`metrics.ndcg_score` now gives a meaningful error message for input of
  length 1.
  :pr:`25672` by :user:`Lene Preuss <lene>` and :user:`Wei-Chun Chu <wcchu>`.

- |Fix| :func:`metrics.log_loss` raises a warning if the values of the parameter
  `y_pred` are not normalized, instead of actually normalizing them in the metric.
  Starting from 1.5 this will raise an error.
  :pr:`25299` by :user:`Omar Salman <OmarManzoor`.

- |Fix| In :func:`metrics.roc_curve`, use the threshold value `np.inf` instead of
  arbitrary `max(y_score) + 1`. This threshold is associated with the ROC curve point
  `tpr=0` and `fpr=0`.
  :pr:`26194` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Fix| The `'matching'` metric has been removed when using SciPy>=1.9
  to be consistent with `scipy.spatial.distance` which does not support
  `'matching'` anymore.
  :pr:`26264` by :user:`Barata T. Onggo <magnusbarata>`

- |API| The `eps` parameter of the :func:`metrics.log_loss` has been deprecated and
  will be removed in 1.5. :pr:`25299` by :user:`Omar Salman <OmarManzoor>`.

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

- |Fix| :class:`gaussian_process.GaussianProcessRegressor` has a new argument
  `n_targets`, which is used to decide the number of outputs when sampling
  from the prior distributions. :pr:`23099` by :user:`Zhehao Liu <MaxwellLZH>`.

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

- |Efficiency| :class:`mixture.GaussianMixture` is more efficient now and will bypass
  unnecessary initialization if the weights, means, and precisions are
  given by users.
  :pr:`26021` by :user:`Jiawei Zhang <jiawei-zhang-a>`.

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

- |MajorFeature| Added the class :class:`model_selection.ValidationCurveDisplay`
  that allows easy plotting of validation curves obtained by the function
  :func:`model_selection.validation_curve`.
  :pr:`25120` by :user:`Guillaume Lemaitre <glemaitre>`.

- |API| The parameter `log_scale` in the class
  :class:`model_selection.LearningCurveDisplay` has been deprecated in 1.3 and
  will be removed in 1.5. The default scale can be overridden by setting it
  directly on the `ax` object and will be set automatically from the spacing
  of the data points otherwise.
  :pr:`25120` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Enhancement| :func:`model_selection.cross_validate` accepts a new parameter
  `return_indices` to return the train-test indices of each cv split.
  :pr:`25659` by :user:`Guillaume Lemaitre <glemaitre>`.

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

- |Fix| :func:`getattr` on :meth:`multioutput.MultiOutputRegressor.partial_fit`
  and :meth:`multioutput.MultiOutputClassifier.partial_fit` now correctly raise
  an `AttributeError` if done before calling `fit`. :pr:`26333` by `Adrin
  Jalali`_.

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

- |Fix| :class:`naive_bayes.GaussianNB` does not raise anymore a `ZeroDivisionError`
  when the provided `sample_weight` reduces the problem to a single class in `fit`.
  :pr:`24140` by :user:`Jonathan Ohayon <Johayon>` and :user:`Chiara Marmo <cmarmo>`.

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

- |Enhancement| The performance of :meth:`neighbors.KNeighborsClassifier.predict`
  and of :meth:`neighbors.KNeighborsClassifier.predict_proba` has been improved
  when `n_neighbors` is large and `algorithm="brute"` with non Euclidean metrics.
  :pr:`24076` by :user:`Meekail Zain <micky774>`, :user:`Julien Jerphanion <jjerphan>`.

- |Fix| Remove support for `KulsinskiDistance` in :class:`neighbors.BallTree`. This
  dissimilarity is not a metric and cannot be supported by the BallTree.
  :pr:`25417` by :user:`Guillaume Lemaitre <glemaitre>`.

- |API| The support for metrics other than `euclidean` and `manhattan` and for
  callables in :class:`neighbors.NearestNeighbors` is deprecated and will be removed in
  version 1.5. :pr:`24083` by :user:`Valentin Laurent <Valentin-Laurent>`.

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

- |Fix| :class:`neural_network.MLPRegressor` and :class:`neural_network.MLPClassifier`
  reports the right `n_iter_` when `warm_start=True`. It corresponds to the number
  of iterations performed on the current call to `fit` instead of the total number
  of iterations performed since the initialization of the estimator.
  :pr:`25443` by :user:`Marvin Krawutschke <Marvvxi>`.

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

- |Feature| :class:`pipeline.FeatureUnion` can now use indexing notation (e.g.
  `feature_union["scalar"]`) to access transformers by name. :pr:`25093` by
  `Thomas Fan`_.

- |Feature| :class:`pipeline.FeatureUnion` can now access the
  `feature_names_in_` attribute if the `X` value seen during `.fit` has a
  `columns` attribute and all columns are strings. e.g. when `X` is a
  `pandas.DataFrame`
  :pr:`25220` by :user:`Ian Thompson <it176131>`.

- |Fix| :meth:`pipeline.Pipeline.fit_transform` now raises an `AttributeError`
  if the last step of the pipeline does not support `fit_transform`.
  :pr:`26325` by `Adrin Jalali`_.

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

- |MajorFeature| Introduces :class:`preprocessing.TargetEncoder` which is a
  categorical encoding based on target mean conditioned on the value of the
  category. :pr:`25334` by `Thomas Fan`_.

- |Feature| :class:`preprocessing.OrdinalEncoder` now supports grouping
  infrequent categories into a single feature. Grouping infrequent categories
  is enabled by specifying how to select infrequent categories with
  `min_frequency` or `max_categories`. :pr:`25677` by `Thomas Fan`_.

- |Enhancement| :class:`preprocessing.PolynomialFeatures` now calculates the
  number of expanded terms a-priori when dealing with sparse `csr` matrices
  in order to optimize the choice of `dtype` for `indices` and `indptr`. It
  can now output `csr` matrices with `np.int32` `indices/indptr` components
  when there are few enough elements, and will automatically use `np.int64`
  for sufficiently large matrices.
  :pr:`20524` by :user:`niuk-a <niuk-a>` and
  :pr:`23731` by :user:`Meekail Zain <micky774>`

- |Enhancement| A new parameter `sparse_output` was added to
  :class:`preprocessing.SplineTransformer`, available as of SciPy 1.8. If
  `sparse_output=True`, :class:`preprocessing.SplineTransformer` returns a sparse
  CSR matrix. :pr:`24145` by :user:`Christian Lorentzen <lorentzenchr>`.

- |Enhancement| Adds a `feature_name_combiner` parameter to
  :class:`preprocessing.OneHotEncoder`. This specifies a custom callable to
  create feature names to be returned by
  :meth:`preprocessing.OneHotEncoder.get_feature_names_out`. The callable
  combines input arguments `(input_feature, category)` to a string.
  :pr:`22506` by :user:`Mario Kostelac <mariokostelac>`.

- |Enhancement| Added support for `sample_weight` in
  :class:`preprocessing.KBinsDiscretizer`. This allows specifying the parameter
  `sample_weight` for each sample to be used while fitting. The option is only
  available when `strategy` is set to `quantile` and `kmeans`.
  :pr:`24935` by :user:`Seladus <seladus>`, :user:`Guillaume Lemaitre <glemaitre>`, and
  :user:`Dea María Léon <deamarialeon>`, :pr:`25257` by :user:`Gleb Levitski <glevv>`.

- |Enhancement| Subsampling through the `subsample` parameter can now be used in
  :class:`preprocessing.KBinsDiscretizer` regardless of the strategy used.
  :pr:`26424` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| :class:`preprocessing.PowerTransformer` now correctly preserves the Pandas
  Index when the `set_config(transform_output="pandas")`. :pr:`26454` by `Thomas Fan`_.

- |Fix| :class:`preprocessing.PowerTransformer` now correctly raises error when
  using `method="box-cox"` on data with a constant `np.nan` column.
  :pr:`26400` by :user:`Yao Xiao <Charlie-XIAO>`.

- |Fix| :class:`preprocessing.PowerTransformer` with `method="yeo-johnson"` now leaves
  constant features unchanged instead of transforming with an arbitrary value for
  the `lambdas_` fitted parameter.
  :pr:`26566` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |API| The default value of the `subsample` parameter of
  :class:`preprocessing.KBinsDiscretizer` will change from `None` to `200_000` in
  version 1.5 when `strategy="kmeans"` or `strategy="uniform"`.
  :pr:`26424` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

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

- |API| `dual` parameter now accepts `auto` option for
  :class:`svm.LinearSVC` and :class:`svm.LinearSVR`.
  :pr:`26093` by :user:`Gleb Levitski <glevv>`.

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

- |MajorFeature| :class:`tree.DecisionTreeRegressor` and
  :class:`tree.DecisionTreeClassifier` support missing values when
  `splitter='best'` and criterion is `gini`, `entropy`, or `log_loss`,
  for classification or `squared_error`, `friedman_mse`, or `poisson`
  for regression. :pr:`23595`, :pr:`26376` by `Thomas Fan`_.

- |Enhancement| Adds a `class_names` parameter to
  :func:`tree.export_text`. This allows specifying the parameter `class_names`
  for each target class in ascending numerical order.
  :pr:`25387` by :user:`William M <Akbeeh>` and :user:`crispinlogan <crispinlogan>`.

- |Fix| :func:`tree.export_graphviz` and :func:`tree.export_text` now accepts
  `feature_names` and `class_names` as array-like rather than lists.
  :pr:`26289` by :user:`Yao Xiao <Charlie-XIAO>`

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

- |FIX| Fixes :func:`utils.check_array` to properly convert pandas
  extension arrays. :pr:`25813` and :pr:`26106` by `Thomas Fan`_.

- |Fix| :func:`utils.check_array` now supports pandas DataFrames with
  extension arrays and object dtypes by return an ndarray with object dtype.
  :pr:`25814` by `Thomas Fan`_.

- |API| `utils.estimator_checks.check_transformers_unfitted_stateless` has been
  introduced to ensure stateless transformers don't raise `NotFittedError`
  during `transform` with no prior call to `fit` or `fit_transform`.
  :pr:`25190` by :user:`Vincent Maladière <Vincent-Maladiere>`.

- |API| A `FutureWarning` is now raised when instantiating a class which inherits from
  a deprecated base class (i.e. decorated by :class:`utils.deprecated`) and which
  overrides the `__init__` method.
  :pr:`25733` by :user:`Brigitta Sipőcz <bsipocz>` and
  :user:`Jérémie du Boisberranger <jeremiedbb>`.

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

- |Enhancement| :meth:`semi_supervised.LabelSpreading.fit` and
  :meth:`semi_supervised.LabelPropagation.fit` now accepts sparse metrics.
  :pr:`19664` by :user:`Kaushik Amar Das <cozek>`.

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

- |Enhancement| Replace obsolete exceptions `EnvironmentError`, `IOError` and
  `WindowsError`.
  :pr:`26466` by :user:`Dimitri Papadopoulos ORfanos <DimitriPapadopoulos>`.

.. rubric:: Code and documentation contributors

Thanks to everyone who has contributed to the maintenance and improvement of
the project since version 1.2, including:

2357juan, Abhishek Singh Kushwah, Adam Handke, Adam Kania, Adam Li, adienes,
Admir Demiraj, adoublet, Adrin Jalali, A.H.Mansouri, Ahmedbgh, Ala-Na, Alex
Buzenet, AlexL, Ali H. El-Kassas, amay, András Simon, André Pedersen, Andrew
Wang, Ankur Singh, annegnx, Ansam Zedan, Anthony22-dev, Artur Hermano, Arturo
Amor, as-90, ashah002, Ashish Dutt, Ashwin Mathur, AymericBasset, Azaria
Gebremichael, Barata Tripramudya Onggo, Benedek Harsanyi, Benjamin Bossan,
Bharat Raghunathan, Binesh Bannerjee, Boris Feld, Brendan Lu, Brevin Kunde,
cache-missing, Camille Troillard, Carla J, carlo, Carlo Lemos, c-git, Changyao
Chen, Chiara Marmo, Christian Lorentzen, Christian Veenhuis, Christine P. Chai,
crispinlogan, Da-Lan, DanGonite57, Dave Berenbaum, davidblnc, david-cortes,
Dayne, Dea María Léon, Denis, Dimitri Papadopoulos Orfanos, Dimitris
Litsidis, Dmitry Nesterov, Dominic Fox, Dominik Prodinger, Edern, Ekaterina
Butyugina, Elabonga Atuo, Emir, farhan khan, Felipe Siola, futurewarning, Gael
Varoquaux, genvalen, Gleb Levitski, Guillaume Lemaitre, gunesbayir, Haesun
Park, hujiahong726, i-aki-y, Ian Thompson, Ido M, Ily, Irene, Jack McIvor,
jakirkham, James Dean, JanFidor, Jarrod Millman, JB Mountford, Jérémie du
Boisberranger, Jessicakk0711, Jiawei Zhang, Joey Ortiz, JohnathanPi, John
Pangas, Joshua Choo Yun Keat, Joshua Hedlund, JuliaSchoepp, Julien Jerphanion,
jygerardy, ka00ri, Kaushik Amar Das, Kento Nozawa, Kian Eliasi, Kilian Kluge,
Lene Preuss, Linus, Logan Thomas, Loic Esteve, Louis Fouquet, Lucy Liu, Madhura
Jayaratne, Marc Torrellas Socastro, Maren Westermann, Mario Kostelac, Mark
Harfouche, Marko Toplak, Marvin Krawutschke, Masanori Kanazu, mathurinm, Matt
Haberland, Max Halford, maximeSaur, Maxwell Liu, m. bou, mdarii, Meekail Zain,
Mikhail Iljin, murezzda, Nawazish Alam, Nicola Fanelli, Nightwalkx, Nikolay
Petrov, Nishu Choudhary, NNLNR, npache, Olivier Grisel, Omar Salman, ouss1508,
PAB, Pandata, partev, Peter Piontek, Phil, pnucci, Pooja M, Pooja Subramaniam,
precondition, Quentin Barthélemy, Rafal Wojdyla, Raghuveer Bhat, Rahil Parikh,
Ralf Gommers, ram vikram singh, Rushil Desai, Sadra Barikbin, SANJAI_3, Sashka
Warner, Scott Gigante, Scott Gustafson, searchforpassion, Seoeun
Hong, Shady el Gewily, Shiva chauhan, Shogo Hida, Shreesha Kumar Bhat, sonnivs,
Sortofamudkip, Stanislav (Stanley) Modrak, Stefanie Senger, Steven Van
Vaerenbergh, Tabea Kossen, Théophile Baranger, Thijs van Weezel, Thomas A
Caswell, Thomas Germer, Thomas J. Fan, Tim Head, Tim P, Tom Dupré la Tour,
tomiock, tspeng, Valentin Laurent, Veghit, VIGNESH D, Vijeth Moudgalya, Vinayak
Mehta, Vincent M, Vincent-violet, Vyom Pathak, William M, windiana42, Xiao
Yuan, Yao Xiao, Yaroslav Halchenko, Yotam Avidar-Constantini, Yuchen Zhou,
Yusuf Raji, zeeshan lone

include    contributors rst     currentmodule   sklearn      release notes 1 3               Version 1 3              For a short description of the main highlights of the release  please refer to  ref  sphx glr auto examples release highlights plot release highlights 1 3 0 py       include   changelog legend inc      changes 1 3 2   Version 1 3 2                  October 2023    Changelog             mod  sklearn datasets                              Fix  All dataset fetchers now accept  data home  as any object that implements   the  class  os PathLike  interface  for instance   class  pathlib Path      pr  27468  by  user  Yao Xiao  Charlie XIAO      mod  sklearn decomposition                                   Fix  Fixes a bug in  class  decomposition KernelPCA  by forcing the output of   the internal  class  preprocessing KernelCenterer  to be a default array  When the   arpack solver is used  it expects an array with a  dtype  attribute     pr  27583  by  user  Guillaume Lemaitre  glemaitre      mod  sklearn metrics                             Fix  Fixes a bug for metrics using  zero division np nan     e g   func   metrics precision score   within a paralell loop    e g   func   model selection cross val score   where the singleton for  np nan    will be different in the sub processes     pr  27573  by  user  Guillaume Lemaitre  glemaitre      mod  sklearn tree                          Fix  Do not leak data via non initialized memory in decision tree pickle files and make   the generation of those files deterministic   pr  27580  by  user  Lo c Est ve  lesteve          changes 1 3 1   Version 1 3 1                  September 2023    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      Fix  Ridge models with  solver  sparse cg   may have slightly different   results with scipy  1 12  because of an underlying change in the scipy solver    see  scipy 18488  https   github com scipy scipy pull 18488    for more   details     pr  26814  by  user  Lo c Est ve  lesteve    Changes impacting all modules                                   Fix  The  set output  API correctly works with list input   pr  27044  by    Thomas Fan     Changelog             mod  sklearn calibration                                 Fix   class  calibration CalibratedClassifierCV  can now handle models that   produce large prediction scores  Before it was numerically unstable     pr  26913  by  user  Omar Salman  OmarManzoor      mod  sklearn cluster                             Fix   class  cluster BisectingKMeans  could crash when predicting on data   with a different scale than the data used to fit the model     pr  27167  by  Olivier Grisel        Fix   class  cluster BisectingKMeans  now works with data that has a single feature     pr  27243  by  user  J r mie du Boisberranger  jeremiedbb      mod  sklearn cross decomposition                                         Fix   class  cross decomposition PLSRegression  now automatically ravels the output   of  predict  if fitted with one dimensional  y      pr  26602  by  user  Yao Xiao  Charlie XIAO      mod  sklearn ensemble                              Fix  Fix a bug in  class  ensemble AdaBoostClassifier  with  algorithm  SAMME     where the decision function of each weak learner should be symmetric  i e    the sum of the scores should sum to zero for a sample      pr  26521  by  user  Guillaume Lemaitre  glemaitre      mod  sklearn feature selection                                       Fix   func  feature selection mutual info regression  now correctly computes the   result when  X  is of integer dtype   pr  26748  by  user  Yao Xiao  Charlie XIAO      mod  sklearn impute                            Fix   class  impute KNNImputer  now correctly adds a missing indicator column in     transform   when   add indicator   is set to   True   and missing values are observed   during   fit     pr  26600  by  user  Shreesha Kumar Bhat  Shreesha3112      mod  sklearn metrics                             Fix  Scorers used with  func  metrics get scorer  handle properly   multilabel indicator matrix     pr  27002  by  user  Guillaume Lemaitre  glemaitre      mod  sklearn mixture                             Fix  The initialization of  class  mixture GaussianMixture  from user provided    precisions init  for  covariance type  of  full  or  tied  was not correct    and has been fixed     pr  26416  by  user  Yang Tao  mchikyt3      mod  sklearn neighbors                               Fix   meth  neighbors KNeighborsClassifier predict  no longer raises an   exception for  pandas DataFrames  input     pr  26772  by  user  J r mie du Boisberranger  jeremiedbb        Fix  Reintroduce  sklearn neighbors BallTree valid metrics  and    sklearn neighbors KDTree valid metrics  as public class attributes     pr  26754  by  user  Julien Jerphanion  jjerphan        Fix   class  sklearn model selection HalvingRandomSearchCV  no longer raises   when the input to the  param distributions  parameter is a list of dicts     pr  26893  by  user  Stefanie Senger  StefanieSenger        Fix  Neighbors based estimators now correctly work when  metric  minkowski   and the   metric parameter  p  is in the range  0   p   1   regardless of the  dtype  of  X      pr  26760  by  user  Shreesha Kumar Bhat  Shreesha3112      mod  sklearn preprocessing                                   Fix   class  preprocessing LabelEncoder  correctly accepts  y  as a keyword   argument   pr  26940  by  Thomas Fan        Fix   class  preprocessing OneHotEncoder  shows a more informative error message   when  sparse output True  and the output is configured to be pandas     pr  26931  by  Thomas Fan      mod  sklearn tree                          Fix   func  tree plot tree  now accepts  class names True  as documented     pr  26903  by  user  Thomas Roehr  2maz       Fix  The  feature names  parameter of  func  tree plot tree  now accepts any kind of   array like instead of just a list   pr  27292  by  user  Rahil Parikh  rprkh         changes 1 3   Version 1 3 0                  June 2023    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      Enhancement   meth  multiclass OutputCodeClassifier predict  now uses a more   efficient pairwise distance reduction  As a consequence  the tie breaking   strategy is different and thus the predicted labels may be different     pr  25196  by  user  Guillaume Lemaitre  glemaitre        Enhancement  The  fit transform  method of  class  decomposition DictionaryLearning    is more efficient but may produce different results as in previous versions when    transform algorithm  is not the same as  fit algorithm  and the number of iterations   is small   pr  24871  by  user  Omar Salman  OmarManzoor        Enhancement  The  sample weight  parameter now will be used in centroids   initialization for  class  cluster KMeans    class  cluster BisectingKMeans    and  class  cluster MiniBatchKMeans     This change will break backward compatibility  since numbers generated   from same random seeds will be different     pr  25752  by  user  Gleb Levitski  glevv       user  J r mie du Boisberranger  jeremiedbb       user  Guillaume Lemaitre  glemaitre        Fix  Treat more consistently small values in the  W  and  H  matrices during the    fit  and  transform  steps of  class  decomposition NMF  and    class  decomposition MiniBatchNMF  which can produce different results than previous   versions   pr  25438  by  user  Yotam Avidar Constantini  yotamcons        Fix   class  decomposition KernelPCA  may produce different results through    inverse transform  if  gamma  is  None   Now it will be chosen correctly as    1 n features  of the data that it is fitted on  while previously it might be   incorrectly chosen as  1 n features  of the data passed to  inverse transform     A new attribute  gamma   is provided for revealing the actual value of  gamma    used each time the kernel is called     pr  26337  by  user  Yao Xiao  Charlie XIAO     Changed displays                      Enhancement   class  model selection LearningCurveDisplay  displays both the   train and test curves by default  You can set  score type  test   to keep the   past behaviour     pr  25120  by  user  Guillaume Lemaitre  glemaitre        Fix   class  model selection ValidationCurveDisplay  now accepts passing a   list to the  param range  parameter     pr  27311  by  user  Arturo Amor  ArturoAmorQ     Changes impacting all modules                                   Enhancement  The  get feature names out  method of the following classes now   raises a  NotFittedError  if the instance is not fitted  This ensures the error is   consistent in all estimators with the  get feature names out  method        class  impute MissingIndicator       class  feature extraction DictVectorizer       class  feature extraction text TfidfTransformer       class  feature selection GenericUnivariateSelect       class  feature selection RFE       class  feature selection RFECV       class  feature selection SelectFdr       class  feature selection SelectFpr       class  feature selection SelectFromModel       class  feature selection SelectFwe       class  feature selection SelectKBest       class  feature selection SelectPercentile       class  feature selection SequentialFeatureSelector       class  feature selection VarianceThreshold       class  kernel approximation AdditiveChi2Sampler       class  impute IterativeImputer       class  impute KNNImputer       class  impute SimpleImputer       class  isotonic IsotonicRegression       class  preprocessing Binarizer       class  preprocessing KBinsDiscretizer       class  preprocessing MaxAbsScaler       class  preprocessing MinMaxScaler       class  preprocessing Normalizer       class  preprocessing OrdinalEncoder       class  preprocessing PowerTransformer       class  preprocessing QuantileTransformer       class  preprocessing RobustScaler       class  preprocessing SplineTransformer       class  preprocessing StandardScaler       class  random projection GaussianRandomProjection       class  random projection SparseRandomProjection     The  NotFittedError  displays an informative message asking to fit the instance   with the appropriate arguments      pr  25294    pr  25308    pr  25291    pr  25367    pr  25402     by  user  John Pangas  jpangas     user  Rahil Parikh  rprkh       and  user  Alex Buzenet  albuzenet        Enhancement  Added a multi threaded Cython routine to the compute squared   Euclidean distances  sometimes followed by a fused reduction operation  for a   pair of datasets consisting of a sparse CSR matrix and a dense NumPy     This can improve the performance of following functions and estimators        func  sklearn metrics pairwise distances argmin       func  sklearn metrics pairwise distances argmin min       class  sklearn cluster AffinityPropagation       class  sklearn cluster Birch       class  sklearn cluster MeanShift       class  sklearn cluster OPTICS       class  sklearn cluster SpectralClustering       func  sklearn feature selection mutual info regression       class  sklearn neighbors KNeighborsClassifier       class  sklearn neighbors KNeighborsRegressor       class  sklearn neighbors RadiusNeighborsClassifier       class  sklearn neighbors RadiusNeighborsRegressor       class  sklearn neighbors LocalOutlierFactor       class  sklearn neighbors NearestNeighbors       class  sklearn manifold Isomap       class  sklearn manifold LocallyLinearEmbedding       class  sklearn manifold TSNE       func  sklearn manifold trustworthiness       class  sklearn semi supervised LabelPropagation       class  sklearn semi supervised LabelSpreading     A typical example of this performance improvement happens when passing a sparse   CSR matrix to the  predict  or  transform  method of estimators that rely on   a dense NumPy representation to store their fitted parameters  or the reverse      For instance   meth  sklearn neighbors NearestNeighbors kneighbors  is now up   to 2 times faster for this case on commonly available laptops      pr  25044  by  user  Julien Jerphanion  jjerphan        Enhancement  All estimators that internally rely on OpenMP multi threading    via Cython  now use a number of threads equal to the number of physical    instead of logical  cores by default  In the past  we observed that using as   many threads as logical cores on SMT hosts could sometimes cause severe   performance problems depending on the algorithms and the shape of the data    Note that it is still possible to manually adjust the number of threads used   by OpenMP as documented in  ref  parallelism       pr  26082  by  user  J r mie du Boisberranger  jeremiedbb   and    user  Olivier Grisel  ogrisel     Experimental   Under Development                                      MajorFeature   ref  Metadata routing  metadata routing   s related base   methods are included in this release  This feature is only available via the    enable metadata routing  feature flag which can be enabled using    func  sklearn set config  and  func  sklearn config context   For now this   feature is mostly useful for third party developers to prepare their code   base for metadata routing  and we strongly recommend that they also hide it   behind the same feature flag  rather than having it enabled by default     pr  24027  by  Adrin Jalali     user  Benjamin Bossan  BenjaminBossan    and    user  Omar Salman  OmarManzoor     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    sklearn                Feature  Added a new option  skip parameter validation   to the function    func  sklearn set config  and context manager  func  sklearn config context   that   allows to skip the validation of the parameters passed to the estimators and public   functions  This can be useful to speed up the code but should be used with care   because it can lead to unexpected behaviors or raise obscure error messages when   setting invalid parameters     pr  25815  by  user  J r mie du Boisberranger  jeremiedbb      mod  sklearn base                          Feature  A    sklearn clone    protocol is now available to override the   default behavior of  func  base clone    pr  24568  by  Thomas Fan        Fix   class  base TransformerMixin  now currently keeps a namedtuple s class   if  transform  returns a namedtuple   pr  26121  by  Thomas Fan      mod  sklearn calibration                                 Fix   class  calibration CalibratedClassifierCV  now does not enforce sample   alignment on  fit params    pr  25805  by  Adrin Jalali      mod  sklearn cluster                             MajorFeature  Added  class  cluster HDBSCAN   a modern hierarchical density based   clustering algorithm  Similarly to  class  cluster OPTICS   it can be seen as a   generalization of  class  cluster DBSCAN  by allowing for hierarchical instead of flat   clustering  however it varies in its approach from  class  cluster OPTICS   This   algorithm is very robust with respect to its hyperparameters  values and can   be used on a wide variety of data without much  if any  tuning     This implementation is an adaptation from the original implementation of HDBSCAN in    scikit learn contrib hdbscan  https   github com scikit learn contrib hdbscan       by  user  Leland McInnes  lmcinnes   et al      pr  26385  by  user  Meekail Zain  micky774       Enhancement  The  sample weight  parameter now will be used in centroids   initialization for  class  cluster KMeans    class  cluster BisectingKMeans    and  class  cluster MiniBatchKMeans     This change will break backward compatibility  since numbers generated   from same random seeds will be different     pr  25752  by  user  Gleb Levitski  glevv       user  J r mie du Boisberranger  jeremiedbb       user  Guillaume Lemaitre  glemaitre        Fix   class  cluster KMeans    class  cluster MiniBatchKMeans  and    func  cluster k means  now correctly handle the combination of  n init  auto     and  init  being an array like  running one initialization in that case     pr  26657  by  user  Binesh Bannerjee  bnsh        API  The  sample weight  parameter in  predict  for    meth  cluster KMeans predict  and  meth  cluster MiniBatchKMeans predict    is now deprecated and will be removed in v1 5     pr  25251  by  user  Gleb Levitski  glevv        API  The  Xred  argument in  func  cluster FeatureAgglomeration inverse transform    is renamed to  Xt  and will be removed in v1 5   pr  26503  by  Adrin Jalali      mod  sklearn compose                             Fix   class  compose ColumnTransformer  raises an informative error when the individual   transformers of  ColumnTransformer  output pandas dataframes with indexes that are   not consistent with each other and the output is configured to be pandas     pr  26286  by  Thomas Fan        Fix   class  compose ColumnTransformer  correctly sets the output of the   remainder when  set output  is called   pr  26323  by  Thomas Fan      mod  sklearn covariance                                Fix  Allows  alpha 0  in  class  covariance GraphicalLasso  to be   consistent with  func  covariance graphical lasso      pr  26033  by  user  Genesis Valencia  genvalen        Fix   func  covariance empirical covariance  now gives an informative   error message when input is not appropriate     pr  26108  by  user  Quentin Barth lemy  qbarthelemy        API  Deprecates  cov init  in  func  covariance graphical lasso  in 1 3 since   the parameter has no effect  It will be removed in 1 5     pr  26033  by  user  Genesis Valencia  genvalen        API  Adds  costs   fitted attribute in  class  covariance GraphicalLasso  and    class  covariance GraphicalLassoCV      pr  26033  by  user  Genesis Valencia  genvalen        API  Adds  covariance  parameter in  class  covariance GraphicalLasso      pr  26033  by  user  Genesis Valencia  genvalen        API  Adds  eps  parameter in  class  covariance GraphicalLasso      func  covariance graphical lasso   and  class  covariance GraphicalLassoCV      pr  26033  by  user  Genesis Valencia  genvalen      mod  sklearn datasets                              Enhancement  Allows to overwrite the parameters used to open the ARFF file using   the parameter  read csv kwargs  in  func  datasets fetch openml  when using the   pandas parser     pr  26433  by  user  Guillaume Lemaitre  glemaitre        Fix   func  datasets fetch openml  returns improved data types when    as frame True  and  parser  liac arff     pr  26386  by  Thomas Fan        Fix  Following the ARFF specs  only the marker       is now considered as a missing   values when opening ARFF files fetched using  func  datasets fetch openml  when using   the pandas parser  The parameter  read csv kwargs  allows to overwrite this behaviour     pr  26551  by  user  Guillaume Lemaitre  glemaitre        Fix   func  datasets fetch openml  will consistently use  np nan  as missing marker   with both parsers   pandas   and   liac arff       pr  26579  by  user  Guillaume Lemaitre  glemaitre        API  The  data transposed  argument of  func  datasets make sparse coded signal    is deprecated and will be removed in v1 5     pr  25784  by  user  J r mie du Boisberranger     mod  sklearn decomposition                                   Efficiency   class  decomposition MiniBatchDictionaryLearning  and    class  decomposition MiniBatchSparsePCA  are now faster for small batch sizes by   avoiding duplicate validations     pr  25490  by  user  J r mie du Boisberranger  jeremiedbb        Enhancement   class  decomposition DictionaryLearning  now accepts the parameter    callback  for consistency with the function  func  decomposition dict learning      pr  24871  by  user  Omar Salman  OmarManzoor        Fix  Treat more consistently small values in the  W  and  H  matrices during the    fit  and  transform  steps of  class  decomposition NMF  and    class  decomposition MiniBatchNMF  which can produce different results than previous   versions   pr  25438  by  user  Yotam Avidar Constantini  yotamcons        API  The  W  argument in  func  decomposition NMF inverse transform  and    class  decomposition MiniBatchNMF inverse transform  is renamed to  Xt  and   will be removed in v1 5   pr  26503  by  Adrin Jalali      mod  sklearn discriminant analysis                                           Enhancement   class  discriminant analysis LinearDiscriminantAnalysis  now   supports the  PyTorch  https   pytorch org       See    ref  array api  for more details   pr  25956  by  Thomas Fan      mod  sklearn ensemble                              Feature   class  ensemble HistGradientBoostingRegressor  now supports   the Gamma deviance loss via  loss  gamma      Using the Gamma deviance as loss function comes in handy for modelling skewed   distributed  strictly positive valued targets     pr  22409  by  user  Christian Lorentzen  lorentzenchr        Feature  Compute a custom out of bag score by passing a callable to    class  ensemble RandomForestClassifier    class  ensemble RandomForestRegressor      class  ensemble ExtraTreesClassifier  and  class  ensemble ExtraTreesRegressor      pr  25177  by  Tim Head        Feature   class  ensemble GradientBoostingClassifier  now exposes   out of bag scores via the  oob scores   or  oob score   attributes     pr  24882  by  user  Ashwin Mathur  awinml        Efficiency   class  ensemble IsolationForest  predict time is now faster    typically by a factor of 8 or more   Internally  the estimator now precomputes   decision path lengths per tree at  fit  time  It is therefore not possible   to load an estimator trained with scikit learn 1 2 to make it predict with   scikit learn 1 3  retraining with scikit learn 1 3 is required     pr  25186  by  user  Felipe Breve Siola  fsiola        Efficiency   class  ensemble RandomForestClassifier  and    class  ensemble RandomForestRegressor  with  warm start True  now only   recomputes out of bag scores when there are actually more  n estimators    in subsequent  fit  calls     pr  26318  by  user  Joshua Choo Yun Keat  choo8        Enhancement   class  ensemble BaggingClassifier  and    class  ensemble BaggingRegressor  expose the  allow nan  tag from the   underlying estimator   pr  25506  by  Thomas Fan        Fix   meth  ensemble RandomForestClassifier fit  sets  max samples   1    when  max samples  is a float and  round n samples   max samples    1      pr  25601  by  user  Jan Fidor  JanFidor        Fix   meth  ensemble IsolationForest fit  no longer warns about missing   feature names when called with  contamination  not   auto   on a pandas   dataframe     pr  25931  by  user  Yao Xiao  Charlie XIAO        Fix   class  ensemble HistGradientBoostingRegressor  and    class  ensemble HistGradientBoostingClassifier  treats negative values for   categorical features consistently as missing values  following LightGBM s and   pandas  conventions     pr  25629  by  Thomas Fan        Fix  Fix deprecation of  base estimator  in  class  ensemble AdaBoostClassifier    and  class  ensemble AdaBoostRegressor  that was introduced in  pr  23819      pr  26242  by  user  Marko Toplak  markotoplak      mod  sklearn exceptions                                Feature  Added  class  exceptions InconsistentVersionWarning  which is raised   when a scikit learn estimator is unpickled with a scikit learn version that is   inconsistent with the sckit learn version the estimator was pickled with     pr  25297  by  Thomas Fan      mod  sklearn feature extraction                                        API   class  feature extraction image PatchExtractor  now follows the   transformer API of scikit learn  This class is defined as a stateless transformer   meaning that it is note required to call  fit  before calling  transform     Parameter validation only happens at  fit  time     pr  24230  by  user  Guillaume Lemaitre  glemaitre      mod  sklearn feature selection                                       Enhancement  All selectors in  mod  sklearn feature selection  will preserve   a DataFrame s dtype when transformed   pr  25102  by  Thomas Fan        Fix   class  feature selection SequentialFeatureSelector  s  cv  parameter   now supports generators   pr  25973  by  Yao Xiao  Charlie XIAO      mod  sklearn impute                            Enhancement  Added the parameter  fill value  to  class  impute IterativeImputer      pr  25232  by  user  Thijs van Weezel  ValueInvestorThijs        Fix   class  impute IterativeImputer  now correctly preserves the Pandas   Index when the  set config transform output  pandas      pr  26454  by  Thomas Fan      mod  sklearn inspection                                Enhancement  Added support for  sample weight  in    func  inspection partial dependence  and    meth  inspection PartialDependenceDisplay from estimator   This allows for   weighted averaging when aggregating for each value of the grid we are making the   inspection on  The option is only available when  method  is set to  brute      pr  25209  and  pr  26644  by  user  Carlo Lemos  vitaliset        API   func  inspection partial dependence  returns a  class  utils Bunch  with   new key   grid values   The  values  key is deprecated in favor of  grid values    and the  values  key will be removed in 1 5     pr  21809  and  pr  25732  by  Thomas Fan      mod  sklearn kernel approximation                                          Fix   class  kernel approximation AdditiveChi2Sampler  is now stateless    The  sample interval   attribute is deprecated and will be removed in 1 5     pr  25190  by  user  Vincent Maladi re  Vincent Maladiere      mod  sklearn linear model                                  Efficiency  Avoid data scaling when  sample weight None  and other   unnecessary data copies and unexpected dense to sparse data conversion in    class  linear model LinearRegression      pr  26207  by  user  Olivier Grisel  ogrisel        Enhancement   class  linear model SGDClassifier      class  linear model SGDRegressor  and  class  linear model SGDOneClassSVM    now preserve dtype for  numpy float32      pr  25587  by  user  Omar Salman  OmarManzoor        Enhancement  The  n iter   attribute has been included in    class  linear model ARDRegression  to expose the actual number of iterations   required to reach the stopping criterion     pr  25697  by  user  John Pangas  jpangas        Fix  Use a more robust criterion to detect convergence of    class  linear model LogisticRegression  with  penalty  l1   and  solver  liblinear     on linearly separable problems     pr  25214  by  Tom Dupre la Tour        Fix  Fix a crash when calling  fit  on    class  linear model LogisticRegression  with  solver  newton cholesky   and    max iter 0  which failed to inspect the state of the model prior to the   first parameter update     pr  26653  by  user  Olivier Grisel  ogrisel        API  Deprecates  n iter  in favor of  max iter  in    class  linear model BayesianRidge  and  class  linear model ARDRegression      n iter  will be removed in scikit learn 1 5  This change makes those   estimators consistent with the rest of estimators     pr  25697  by  user  John Pangas  jpangas      mod  sklearn manifold                              Fix   class  manifold Isomap  now correctly preserves the Pandas   Index when the  set config transform output  pandas      pr  26454  by  Thomas Fan      mod  sklearn metrics                             Feature  Adds  zero division np nan  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   When  zero division np nan  and there is a   zero division  the metric is undefined and is excluded from averaging  When not used   for averages  the value returned is  np nan      pr  25531  by  user  Marc Torrellas Socastro  marctorsoc        Feature   func  metrics average precision score  now supports the   multiclass case     pr  17388  by  user  Geoffrey Bolmier  gbolmier   and    pr  24769  by  user  Ashwin Mathur  awinml        Efficiency  The computation of the expected mutual information in    func  metrics adjusted mutual info score  is now faster when the number of   unique labels is large and its memory usage is reduced in general     pr  25713  by  user  Kshitij Mathur  Kshitij68       user  Guillaume Lemaitre  glemaitre     user  Omar Salman  OmarManzoor   and    user  J r mie du Boisberranger  jeremiedbb        Enhancement   class  metrics silhouette samples  nows accepts a sparse   matrix of pairwise distances between samples  or a feature array     pr  18723  by  user  Sahil Gupta  sahilgupta2105   and    pr  24677  by  user  Ashwin Mathur  awinml        Enhancement  A new parameter  drop intermediate  was added to    func  metrics precision recall curve      func  metrics PrecisionRecallDisplay from estimator      func  metrics PrecisionRecallDisplay from predictions     which drops some suboptimal thresholds to create lighter precision recall   curves     pr  24668  by  user  dberenbaum       Enhancement   meth  metrics RocCurveDisplay from estimator  and    meth  metrics RocCurveDisplay from predictions  now accept two new keywords     plot chance level  and  chance level kw  to plot the baseline chance   level  This line is exposed in the  chance level   attribute     pr  25987  by  user  Yao Xiao  Charlie XIAO        Enhancement   meth  metrics PrecisionRecallDisplay from estimator  and    meth  metrics PrecisionRecallDisplay from predictions  now accept two new   keywords   plot chance level  and  chance level kw  to plot the baseline   chance level  This line is exposed in the  chance level   attribute     pr  26019  by  user  Yao Xiao  Charlie XIAO        Fix   func  metrics pairwise manhattan distances  now supports readonly sparse datasets     pr  25432  by  user  Julien Jerphanion  jjerphan        Fix  Fixed  func  metrics classification report  so that empty input will return    np nan   Previously   macro avg  and  weighted avg  would return   e g   f1 score np nan  and  f1 score 0 0   being inconsistent  Now  they   both return  np nan      pr  25531  by  user  Marc Torrellas Socastro  marctorsoc        Fix   func  metrics ndcg score  now gives a meaningful error message for input of   length 1     pr  25672  by  user  Lene Preuss  lene   and  user  Wei Chun Chu  wcchu        Fix   func  metrics log loss  raises a warning if the values of the parameter    y pred  are not normalized  instead of actually normalizing them in the metric    Starting from 1 5 this will raise an error     pr  25299  by  user  Omar Salman  OmarManzoor       Fix  In  func  metrics roc curve   use the threshold value  np inf  instead of   arbitrary  max y score    1   This threshold is associated with the ROC curve point    tpr 0  and  fpr 0      pr  26194  by  user  Guillaume Lemaitre  glemaitre        Fix  The   matching   metric has been removed when using SciPy  1 9   to be consistent with  scipy spatial distance  which does not support     matching   anymore     pr  26264  by  user  Barata T  Onggo  magnusbarata       API  The  eps  parameter of the  func  metrics log loss  has been deprecated and   will be removed in 1 5   pr  25299  by  user  Omar Salman  OmarManzoor      mod  sklearn gaussian process                                      Fix   class  gaussian process GaussianProcessRegressor  has a new argument    n targets   which is used to decide the number of outputs when sampling   from the prior distributions   pr  23099  by  user  Zhehao Liu  MaxwellLZH      mod  sklearn mixture                             Efficiency   class  mixture GaussianMixture  is more efficient now and will bypass   unnecessary initialization if the weights  means  and precisions are   given by users     pr  26021  by  user  Jiawei Zhang  jiawei zhang a      mod  sklearn model selection                                     MajorFeature  Added the class  class  model selection ValidationCurveDisplay    that allows easy plotting of validation curves obtained by the function    func  model selection validation curve      pr  25120  by  user  Guillaume Lemaitre  glemaitre        API  The parameter  log scale  in the class    class  model selection LearningCurveDisplay  has been deprecated in 1 3 and   will be removed in 1 5  The default scale can be overridden by setting it   directly on the  ax  object and will be set automatically from the spacing   of the data points otherwise     pr  25120  by  user  Guillaume Lemaitre  glemaitre        Enhancement   func  model selection cross validate  accepts a new parameter    return indices  to return the train test indices of each cv split     pr  25659  by  user  Guillaume Lemaitre  glemaitre      mod  sklearn multioutput                                 Fix   func  getattr  on  meth  multioutput MultiOutputRegressor partial fit    and  meth  multioutput MultiOutputClassifier partial fit  now correctly raise   an  AttributeError  if done before calling  fit    pr  26333  by  Adrin   Jalali      mod  sklearn naive bayes                                 Fix   class  naive bayes GaussianNB  does not raise anymore a  ZeroDivisionError    when the provided  sample weight  reduces the problem to a single class in  fit      pr  24140  by  user  Jonathan Ohayon  Johayon   and  user  Chiara Marmo  cmarmo      mod  sklearn neighbors                               Enhancement  The performance of  meth  neighbors KNeighborsClassifier predict    and of  meth  neighbors KNeighborsClassifier predict proba  has been improved   when  n neighbors  is large and  algorithm  brute   with non Euclidean metrics     pr  24076  by  user  Meekail Zain  micky774     user  Julien Jerphanion  jjerphan        Fix  Remove support for  KulsinskiDistance  in  class  neighbors BallTree   This   dissimilarity is not a metric and cannot be supported by the BallTree     pr  25417  by  user  Guillaume Lemaitre  glemaitre        API  The support for metrics other than  euclidean  and  manhattan  and for   callables in  class  neighbors NearestNeighbors  is deprecated and will be removed in   version 1 5   pr  24083  by  user  Valentin Laurent  Valentin Laurent      mod  sklearn neural network                                    Fix   class  neural network MLPRegressor  and  class  neural network MLPClassifier    reports the right  n iter   when  warm start True   It corresponds to the number   of iterations performed on the current call to  fit  instead of the total number   of iterations performed since the initialization of the estimator     pr  25443  by  user  Marvin Krawutschke  Marvvxi      mod  sklearn pipeline                              Feature   class  pipeline FeatureUnion  can now use indexing notation  e g     feature union  scalar     to access transformers by name   pr  25093  by    Thomas Fan        Feature   class  pipeline FeatureUnion  can now access the    feature names in   attribute if the  X  value seen during   fit  has a    columns  attribute and all columns are strings  e g  when  X  is a    pandas DataFrame     pr  25220  by  user  Ian Thompson  it176131        Fix   meth  pipeline Pipeline fit transform  now raises an  AttributeError    if the last step of the pipeline does not support  fit transform      pr  26325  by  Adrin Jalali      mod  sklearn preprocessing                                   MajorFeature  Introduces  class  preprocessing TargetEncoder  which is a   categorical encoding based on target mean conditioned on the value of the   category   pr  25334  by  Thomas Fan        Feature   class  preprocessing OrdinalEncoder  now supports grouping   infrequent categories into a single feature  Grouping infrequent categories   is enabled by specifying how to select infrequent categories with    min frequency  or  max categories    pr  25677  by  Thomas Fan        Enhancement   class  preprocessing PolynomialFeatures  now calculates the   number of expanded terms a priori when dealing with sparse  csr  matrices   in order to optimize the choice of  dtype  for  indices  and  indptr   It   can now output  csr  matrices with  np int32   indices indptr  components   when there are few enough elements  and will automatically use  np int64    for sufficiently large matrices     pr  20524  by  user  niuk a  niuk a   and    pr  23731  by  user  Meekail Zain  micky774       Enhancement  A new parameter  sparse output  was added to    class  preprocessing SplineTransformer   available as of SciPy 1 8  If    sparse output True    class  preprocessing SplineTransformer  returns a sparse   CSR matrix   pr  24145  by  user  Christian Lorentzen  lorentzenchr        Enhancement  Adds a  feature name combiner  parameter to    class  preprocessing OneHotEncoder   This specifies a custom callable to   create feature names to be returned by    meth  preprocessing OneHotEncoder get feature names out   The callable   combines input arguments   input feature  category   to a string     pr  22506  by  user  Mario Kostelac  mariokostelac        Enhancement  Added support for  sample weight  in    class  preprocessing KBinsDiscretizer   This allows specifying the parameter    sample weight  for each sample to be used while fitting  The option is only   available when  strategy  is set to  quantile  and  kmeans      pr  24935  by  user  Seladus  seladus     user  Guillaume Lemaitre  glemaitre    and    user  Dea Mar a L on  deamarialeon     pr  25257  by  user  Gleb Levitski  glevv        Enhancement  Subsampling through the  subsample  parameter can now be used in    class  preprocessing KBinsDiscretizer  regardless of the strategy used     pr  26424  by  user  J r mie du Boisberranger  jeremiedbb        Fix   class  preprocessing PowerTransformer  now correctly preserves the Pandas   Index when the  set config transform output  pandas      pr  26454  by  Thomas Fan        Fix   class  preprocessing PowerTransformer  now correctly raises error when   using  method  box cox   on data with a constant  np nan  column     pr  26400  by  user  Yao Xiao  Charlie XIAO        Fix   class  preprocessing PowerTransformer  with  method  yeo johnson   now leaves   constant features unchanged instead of transforming with an arbitrary value for   the  lambdas   fitted parameter     pr  26566  by  user  J r mie du Boisberranger  jeremiedbb        API  The default value of the  subsample  parameter of    class  preprocessing KBinsDiscretizer  will change from  None  to  200 000  in   version 1 5 when  strategy  kmeans   or  strategy  uniform       pr  26424  by  user  J r mie du Boisberranger  jeremiedbb      mod  sklearn svm                         API   dual  parameter now accepts  auto  option for    class  svm LinearSVC  and  class  svm LinearSVR      pr  26093  by  user  Gleb Levitski  glevv      mod  sklearn tree                          MajorFeature   class  tree DecisionTreeRegressor  and    class  tree DecisionTreeClassifier  support missing values when    splitter  best   and criterion is  gini    entropy   or  log loss     for classification or  squared error    friedman mse   or  poisson    for regression   pr  23595    pr  26376  by  Thomas Fan        Enhancement  Adds a  class names  parameter to    func  tree export text   This allows specifying the parameter  class names    for each target class in ascending numerical order     pr  25387  by  user  William M  Akbeeh   and  user  crispinlogan  crispinlogan        Fix   func  tree export graphviz  and  func  tree export text  now accepts    feature names  and  class names  as array like rather than lists     pr  26289  by  user  Yao Xiao  Charlie XIAO     mod  sklearn utils                           FIX  Fixes  func  utils check array  to properly convert pandas   extension arrays   pr  25813  and  pr  26106  by  Thomas Fan        Fix   func  utils check array  now supports pandas DataFrames with   extension arrays and object dtypes by return an ndarray with object dtype     pr  25814  by  Thomas Fan        API   utils estimator checks check transformers unfitted stateless  has been   introduced to ensure stateless transformers don t raise  NotFittedError    during  transform  with no prior call to  fit  or  fit transform      pr  25190  by  user  Vincent Maladi re  Vincent Maladiere        API  A  FutureWarning  is now raised when instantiating a class which inherits from   a deprecated base class  i e  decorated by  class  utils deprecated   and which   overrides the    init    method     pr  25733  by  user  Brigitta Sip cz  bsipocz   and    user  J r mie du Boisberranger  jeremiedbb      mod  sklearn semi supervised                                     Enhancement   meth  semi supervised LabelSpreading fit  and    meth  semi supervised LabelPropagation fit  now accepts sparse metrics     pr  19664  by  user  Kaushik Amar Das  cozek     Miscellaneous                   Enhancement  Replace obsolete exceptions  EnvironmentError    IOError  and    WindowsError      pr  26466  by  user  Dimitri Papadopoulos ORfanos  DimitriPapadopoulos        rubric   Code and documentation contributors  Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1 2  including   2357juan  Abhishek Singh Kushwah  Adam Handke  Adam Kania  Adam Li  adienes  Admir Demiraj  adoublet  Adrin Jalali  A H Mansouri  Ahmedbgh  Ala Na  Alex Buzenet  AlexL  Ali H  El Kassas  amay  Andr s Simon  Andr  Pedersen  Andrew Wang  Ankur Singh  annegnx  Ansam Zedan  Anthony22 dev  Artur Hermano  Arturo Amor  as 90  ashah002  Ashish Dutt  Ashwin Mathur  AymericBasset  Azaria Gebremichael  Barata Tripramudya Onggo  Benedek Harsanyi  Benjamin Bossan  Bharat Raghunathan  Binesh Bannerjee  Boris Feld  Brendan Lu  Brevin Kunde  cache missing  Camille Troillard  Carla J  carlo  Carlo Lemos  c git  Changyao Chen  Chiara Marmo  Christian Lorentzen  Christian Veenhuis  Christine P  Chai  crispinlogan  Da Lan  DanGonite57  Dave Berenbaum  davidblnc  david cortes  Dayne  Dea Mar a L on  Denis  Dimitri Papadopoulos Orfanos  Dimitris Litsidis  Dmitry Nesterov  Dominic Fox  Dominik Prodinger  Edern  Ekaterina Butyugina  Elabonga Atuo  Emir  farhan khan  Felipe Siola  futurewarning  Gael Varoquaux  genvalen  Gleb Levitski  Guillaume Lemaitre  gunesbayir  Haesun Park  hujiahong726  i aki y  Ian Thompson  Ido M  Ily  Irene  Jack McIvor  jakirkham  James Dean  JanFidor  Jarrod Millman  JB Mountford  J r mie du Boisberranger  Jessicakk0711  Jiawei Zhang  Joey Ortiz  JohnathanPi  John Pangas  Joshua Choo Yun Keat  Joshua Hedlund  JuliaSchoepp  Julien Jerphanion  jygerardy  ka00ri  Kaushik Amar Das  Kento Nozawa  Kian Eliasi  Kilian Kluge  Lene Preuss  Linus  Logan Thomas  Loic Esteve  Louis Fouquet  Lucy Liu  Madhura Jayaratne  Marc Torrellas Socastro  Maren Westermann  Mario Kostelac  Mark Harfouche  Marko Toplak  Marvin Krawutschke  Masanori Kanazu  mathurinm  Matt Haberland  Max Halford  maximeSaur  Maxwell Liu  m  bou  mdarii  Meekail Zain  Mikhail Iljin  murezzda  Nawazish Alam  Nicola Fanelli  Nightwalkx  Nikolay Petrov  Nishu Choudhary  NNLNR  npache  Olivier Grisel  Omar Salman  ouss1508  PAB  Pandata  partev  Peter Piontek  Phil  pnucci  Pooja M  Pooja Subramaniam  precondition  Quentin Barth lemy  Rafal Wojdyla  Raghuveer Bhat  Rahil Parikh  Ralf Gommers  ram vikram singh  Rushil Desai  Sadra Barikbin  SANJAI 3  Sashka Warner  Scott Gigante  Scott Gustafson  searchforpassion  Seoeun Hong  Shady el Gewily  Shiva chauhan  Shogo Hida  Shreesha Kumar Bhat  sonnivs  Sortofamudkip  Stanislav  Stanley  Modrak  Stefanie Senger  Steven Van Vaerenbergh  Tabea Kossen  Th ophile Baranger  Thijs van Weezel  Thomas A Caswell  Thomas Germer  Thomas J  Fan  Tim Head  Tim P  Tom Dupr  la Tour  tomiock  tspeng  Valentin Laurent  Veghit  VIGNESH D  Vijeth Moudgalya  Vinayak Mehta  Vincent M  Vincent violet  Vyom Pathak  William M  windiana42  Xiao Yuan  Yao Xiao  Yaroslav Halchenko  Yotam Avidar Constantini  Yuchen Zhou  Yusuf Raji  zeeshan lone