v0.20.rst

.. currentmodule:: sklearn

Version 0.20.0

September, 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!

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:`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)
• :class:`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:`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.

Changelog

Support for Python 3.3 has been officially dropped.

:mod:`sklearn.cluster`

• |MajorFeature| A new clustering algorithm: :class:`cluster.OPTICS`: an algoritm related to :class:`cluster.DBSCAN`, that has hyperparameters easier to set and that scales better, by :user:`Shane <espg>`.
• |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 :func:`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 :func:`covariance.graph_lasso`, :class:`covariance.GraphLasso` and :class:`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. 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:`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 :func:`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 :func:`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 repsectively 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 :func:`_class_means` and :func:`_class_cov` in :mod:`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>`.

: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 :class:`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>`.
• |API| The parameters min_samples_leaf and min_weight_fraction_leaf in tree-based ensembles are deprecated and will be removed (fixed to 1 and 0 respectively) in version 0.22. These parameters were not effective for regularization and at worst would produce bad splits. :issue:`10773` by :user:`Bob Chen <lasagnaman>` and `Joel Nothman`_.
• |Fix| :class:`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 :term:`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 :func:`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| :func:`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 :func:`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 :func:`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 :class:`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 :class:`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 :func:`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:`Nearest neighbors <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 avaiable. 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 :class:`BinaryTree`. :issue:`11774` by :user:`Nicolas Hug <NicolasHug>`.

:mod:`sklearn.neural_network`

• |Feature| Add n_iter_no_change parameter in :class:`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 :class:`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>`.

: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 :term:`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 :class:`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 :class:`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 :class:`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), :class:`tree._criterion.ClassificationCriterion` and :class:`tree._criterion.RegressionCriterion` may now be cimported and extended. :issue:`10325` by :user:`Camil Staps <camilstaps>`.
• |Fix| Fixed a bug in :class:`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 :class:`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>`.
• |API| The parameters min_samples_leaf and min_weight_fraction_leaf in :class:`tree.DecisionTreeClassifier` and :class:`tree.DecisionTreeRegressor` are deprecated and will be removed (fixed to 1 and 0 respectively) in version 0.22. These parameters were not effective for regularization and at worst would produce bad splits. :issue:`10773` by :user:`Bob Chen <lasagnaman>` and `Joel Nothman`_.

: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 :func:`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. 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`_.

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>`.