.. currentmodule:: sklearn
In Development
- |Fix| :class:`cluster.Birch`, :class:`feature_selection.RFECV`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.GradientBoostingRegressor`, and :class:`ensemble.GradientBoostingClassifier` do not raise warning when fitted on a pandas DataFrame anymore. :pr:`21578` by `Thomas Fan`_.
- |Fix| Fixed an infinite loop in :func:`cluster.SpectralClustering` by moving an iteration counter from try to except. :pr:`21271` by :user:`Tyler Martin <martintb>`.
- |Fix| :func:`datasets.fetch_openml` is now thread safe. Data is first downloaded to a temporary subfolder and then renamed. :pr:`21833` by :user:`Siavash Rezazadeh <siavrez>`.
- |Fix| Fixed the constraint on the objective function of :class:`decomposition.DictionaryLearning`, :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.SparsePCA` and :class:`decomposition.MiniBatchSparsePCA` to be convex and match the referenced article. :pr:`19210` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| :class:`ensemble.RandomForestClassifier`,
:class:`ensemble.RandomForestRegressor`,
:class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`,
and :class:`ensemble.RandomTreesEmbedding` now raise a
ValueErrorwhenbootstrap=Falseandmax_samplesis notNone. :pr:`21295` :user:`Haoyin Xu <PSSF23>`. - |Fix| Solve a bug in :class:`ensemble.GradientBoostingClassifier` where the exponential loss was computing the positive gradient instead of the negative one. :pr:`22050` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| Fixed :class:`feature_selection.SelectFromModel` by improving support for base estimators that do not set feature_names_in_. :pr:`21991` by `Thomas Fan`_.
- |Fix| Fix a bug in :class:`linear_model.RidgeClassifierCV` where the method predict was performing an argmax on the scores obtained from decision_function instead of returning the multilabel indicator matrix. :pr:`19869` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :class:`linear_model.LassoLarsIC` now correctly computes AIC and BIC. An error is now raised when n_features > n_samples and when the noise variance is not provided. :pr:`21481` by :user:`Guillaume Lemaitre <glemaitre>` and :user:`Andrés Babino <ababino>`.
- |Fix| Fixed an unnecessary error when fitting :class:`manifold.Isomap` with a precomputed dense distance matrix where the neighbors graph has multiple disconnected components. :pr:`21915` by `Tom Dupre la Tour`_.
- |Fix| All :class:`sklearn.metrics.DistanceMetric` subclasses now correctly support read-only buffer attributes. This fixes a regression introduced in 1.0.0 with respect to 0.24.2. :pr:`21694` by :user:`Julien Jerphanion <jjerphan>`.
- |Fix| All :class:`sklearn.metrics.MinkowskiDistance` now accepts a weight parameter that makes it possible to write code that behaves consistently both with scipy 1.8 and earlier versions. In turns this means that all neighbors-based estimators (except those that use algorithm="kd_tree") now accept a weight parameter with metric="minknowski" to yield results that are always consistent with scipy.spatial.distance.cdist. :pr:`21741` by :user:`Olivier Grisel <ogrisel>`.
- |Fix| :meth:`multiclass.OneVsRestClassifier.predict_proba` does not error when fitted on constant integer targets. :pr:`21871` by `Thomas Fan`_.
- |Fix| :class:`neighbors.KDTree` and :class:`neighbors.BallTree` correctly supports read-only buffer attributes. :pr:`21845` by `Thomas Fan`_.
- |Fix| Fixes compatibility bug with NumPy 1.22 in :class:`preprocessing.OneHotEncoder`. :pr:`21517` by `Thomas Fan`_.
- |Fix| Prevents :func:`tree.plot_tree` from drawing out of the boundary of the figure. :pr:`21917` by `Thomas Fan`_.
- |Fix| Support loading pickles of decision tree models when the pickle has been generated on a platform with a different bitness. A typical example is to train and pickle the model on 64 bit machine and load the model on a 32 bit machine for prediction. :pr:`21552` by :user:`Loïc Estève <lesteve>`.
- |Fix| :func:`utils.estimator_html_repr` now escapes all the estimator descriptions in the generated HTML. :pr:`21493` by :user:`Aurélien Geron <ageron>`.
October 2021
- |Fix| Non-fit methods in the following classes do not raise a UserWarning when fitted on DataFrames with valid feature names: :class:`covariance.EllipticEnvelope`, :class:`ensemble.IsolationForest`, :class:`ensemble.AdaBoostClassifier`, :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor`. :pr:`21199` by `Thomas Fan`_.
- |Fix| Fixed :class:`calibration.CalibratedClassifierCV` to take into account sample_weight when computing the base estimator prediction when ensemble=False. :pr:`20638` by :user:`Julien Bohné <JulienB-78>`.
- |Fix| Fixed a bug in :class:`calibration.CalibratedClassifierCV` with method="sigmoid" that was ignoring the sample_weight when computing the the Bayesian priors. :pr:`21179` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| Fixed a bug in :class:`cluster.KMeans`, ensuring reproducibility and equivalence between sparse and dense input. :pr:`21195` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixed a bug that could produce a segfault in rare cases for :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. :pr:`21130` :user:`Christian Lorentzen <lorentzenchr>`.
- |Fix| Compute y_std properly with multi-target in :class:`sklearn.gaussian_process.GaussianProcessRegressor` allowing proper normalization in multi-target scene. :pr:`20761` by :user:`Patrick de C. T. R. Ferreira <patrickctrf>`.
- |Efficiency| Fixed an efficiency regression introduced in version 1.0.0 in the transform method of :class:`feature_extraction.text.CountVectorizer` which no longer checks for uppercase characters in the provided vocabulary. :pr:`21251` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixed a bug in :class:`feature_extraction.CountVectorizer` and :class:`feature_extraction.TfidfVectorizer` by raising an error when 'min_idf' or 'max_idf' are floating-point numbers greater than 1. :pr:`20752` by :user:`Alek Lefebvre <AlekLefebvre>`.
- |Fix| Improves stability of :class:`linear_model.LassoLars` for different versions of openblas. :pr:`21340` by `Thomas Fan`_.
- |Fix| :class:`linear_model.LogisticRegression` now raises a better error message when the solver does not support sparse matrices with int64 indices. :pr:`21093` by `Tom Dupre la Tour`_.
- |Fix| :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor` with metric="precomputed" raises an error for bsr and dok sparse matrices in methods: fit, kneighbors and radius_neighbors, due to handling of explicit zeros in bsr and dok :term:`sparse graph` formats. :pr:`21199` by `Thomas Fan`_.
- |Fix| :meth:`pipeline.Pipeline.get_feature_names_out` correctly passes feature names out from one step of a pipeline to the next. :pr:`21351` by `Thomas Fan`_.
- |Fix| :class:`svm.SVC` and :class:`svm.SVR` check for an inconsistency in its internal representation and raise an error instead of segfaulting. This fix also resolves CVE-2020-28975. :pr:`21336` by `Thomas Fan`_.
- |Enhancement| :func:`utils.validation._check_sample_weight` can perform a non-negativity check on the sample weights. It can be turned on using the only_non_negative bool parameter. Estimators that check for non-negative weights are updated: :func:`linear_model.LinearRegression` (here the previous error message was misleading), :func:`ensemble.AdaBoostClassifier`, :func:`ensemble.AdaBoostRegressor`, :func:`neighbors.KernelDensity`. :pr:`20880` by :user:`Guillaume Lemaitre <glemaitre>` and :user:`András Simon <simonandras>`.
- |Fix| Solve a bug in :func:`~sklearn.utils.metaestimators.if_delegate_has_method` where the underlying check for an attribute did not work with NumPy arrays. :pr:`21145` by :user:`Zahlii <Zahlii>`.
- |Fix| Fitting an estimator on a dataset that has no feature names, that was previously fitted on a dataset with feature names no longer keeps the old feature names stored in the feature_names_in_ attribute. :pr:`21389` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
September 2021
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_0_0.py`.
Version 1.0.0 of scikit-learn requires python 3.7+, numpy 1.14.6+ and scipy 1.1.0+. Optional minimal dependency is matplotlib 2.2.2+.
In an effort to promote clear and non-ambiguous use of the library, most constructor and function parameters must now be passed as keyword arguments (i.e. using the param=value syntax) instead of positional. If a keyword-only parameter is used as positional, a TypeError is now raised. :issue:`15005` :pr:`20002` by `Joel Nothman`_, `Adrin Jalali`_, `Thomas Fan`_, `Nicolas Hug`_, and `Tom Dupre la Tour`_. See SLEP009 for more details.
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| :class:`manifold.TSNE` now avoids numerical underflow issues during affinity matrix computation.
- |Fix| :class:`manifold.Isomap` now connects disconnected components of the neighbors graph along some minimum distance pairs, instead of changing every infinite distances to zero.
- |Fix| The splitting criterion of :class:`tree.DecisionTreeClassifier` and :class:`tree.DecisionTreeRegressor` can be impacted by a fix in the handling of rounding errors. Previously some extra spurious splits could occur.
- |Fix| :func:`model_selection.train_test_split` with a stratify parameter and :class:`model_selection.StratifiedShuffleSplit` may lead to slightly different results.
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.)
- |API| The option for using the squared error via
lossandcriterionparameters was made more consistent. The preferred way is by setting the value to "squared_error". Old option names are still valid, produce the same models, but are deprecated and will be removed in version 1.2. :pr:`19310` by :user:`Christian Lorentzen <lorentzenchr>`.- For :class:`ensemble.ExtraTreesRegressor`, criterion="mse" is deprecated, use "squared_error" instead which is now the default.
- For :class:`ensemble.GradientBoostingRegressor`, loss="ls" is deprecated, use "squared_error" instead which is now the default.
- For :class:`ensemble.RandomForestRegressor`, criterion="mse" is deprecated, use "squared_error" instead which is now the default.
- For :class:`ensemble.HistGradientBoostingRegressor`, loss="least_squares" is deprecated, use "squared_error" instead which is now the default.
- For :class:`linear_model.RANSACRegressor`, loss="squared_loss" is deprecated, use "squared_error" instead.
- For :class:`linear_model.SGDRegressor`, loss="squared_loss" is deprecated, use "squared_error" instead which is now the default.
- For :class:`tree.DecisionTreeRegressor`, criterion="mse" is deprecated, use "squared_error" instead which is now the default.
- For :class:`tree.ExtraTreeRegressor`, criterion="mse" is deprecated, use "squared_error" instead which is now the default.
- |API| The option for using the absolute error via
lossandcriterionparameters was made more consistent. The preferred way is by setting the value to "absolute_error". Old option names are still valid, produce the same models, but are deprecated and will be removed in version 1.2. :pr:`19733` by :user:`Christian Lorentzen <lorentzenchr>`.- For :class:`ensemble.ExtraTreesRegressor`, criterion="mae" is deprecated, use "absolute_error" instead.
- For :class:`ensemble.GradientBoostingRegressor`, loss="lad" is deprecated, use "absolute_error" instead.
- For :class:`ensemble.RandomForestRegressor`, criterion="mae" is deprecated, use "absolute_error" instead.
- For :class:`ensemble.HistGradientBoostingRegressor`, loss="least_absolute_deviation" is deprecated, use "absolute_error" instead.
- For :class:`linear_model.RANSACRegressor`, loss="absolute_loss" is deprecated, use "absolute_error" instead which is now the default.
- For :class:`tree.DecisionTreeRegressor`, criterion="mae" is deprecated, use "absolute_error" instead.
- For :class:`tree.ExtraTreeRegressor`, criterion="mae" is deprecated, use "absolute_error" instead.
- |API| np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. :pr:`20165` by `Thomas Fan`_.
- |API| :term:`get_feature_names_out` has been added to the transformer API to get the names of the output features. get_feature_names has in turn been deprecated. :pr:`18444` by `Thomas Fan`_.
- |API| All estimators store feature_names_in_ when fitted on pandas Dataframes.
These feature names are compared to names seen in non-fit methods, e.g.
transform and will raise a FutureWarning if they are not consistent.
These
FutureWarnings will becomeValueErrors in 1.2. :pr:`18010` by `Thomas Fan`_.
- |Fix| :func:`config_context` is now threadsafe. :pr:`18736` by `Thomas Fan`_.
- |Feature| :func:`calibration.CalibrationDisplay` added to plot calibration curves. :pr:`17443` by :user:`Lucy Liu <lucyleeow>`.
- |Fix| The
predictandpredict_probamethods of :class:`calibration.CalibratedClassifierCV` can now properly be used on prefitted pipelines. :pr:`19641` by :user:`Alek Lefebvre <AlekLefebvre>`. - |Fix| Fixed an error when using a :class:`ensemble.VotingClassifier` as base_estimator in :class:`calibration.CalibratedClassifierCV`. :pr:`20087` by :user:`Clément Fauchereau <clement-f>`.
- |Efficiency| The
"k-means++"initialization of :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans` is now faster, especially in multicore settings. :pr:`19002` by :user:`Jon Crall <Erotemic>` and :user:`Jérémie du Boisberranger <jeremiedbb>`. - |Efficiency| :class:`cluster.KMeans` with algorithm='elkan' is now faster in multicore settings. :pr:`19052` by :user:`Yusuke Nagasaka <YusukeNagasaka>`.
- |Efficiency| :class:`cluster.MiniBatchKMeans` is now faster in multicore settings. :pr:`17622` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Efficiency| :class:`cluster.OPTICS` can now cache the output of the computation of the tree, using the memory parameter. :pr:`19024` by :user:`Frankie Robertson <frankier>`.
- |Enhancement| The predict and fit_predict methods of :class:`cluster.AffinityPropagation` now accept sparse data type for input data. :pr:`20117` by :user:`Venkatachalam Natchiappan <venkyyuvy>`
- |Fix| Fixed a bug in :class:`cluster.MiniBatchKMeans` where the sample weights were partially ignored when the input is sparse. :pr:`17622` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Improved convergence detection based on center change in :class:`cluster.MiniBatchKMeans` which was almost never achievable. :pr:`17622` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |FIX| :class:`cluster.AgglomerativeClustering` now supports readonly memory-mapped datasets. :pr:`19883` by :user:`Julien Jerphanion <jjerphan>`.
- |Fix| :class:`cluster.AgglomerativeClustering` correctly connects components when connectivity and affinity are both precomputed and the number of connected components is greater than 1. :pr:`20597` by `Thomas Fan`_.
- |Fix| :class:`cluster.FeatureAgglomeration` does not accept a
**paramskwarg in thefitfunction anymore, resulting in a more concise error message. :pr:`20899` by :user:`Adam Li <adam2392>`. - |Fix| Fixed a bug in :class:`cluster.KMeans`, ensuring reproducibility and equivalence between sparse and dense input. :pr:`20200` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| :class:`cluster.Birch` attributes, fit_ and partial_fit_, are deprecated and will be removed in 1.2. :pr:`19297` by `Thomas Fan`_.
- |API| the default value for the batch_size parameter of :class:`cluster.MiniBatchKMeans` was changed from 100 to 1024 due to efficiency reasons. The n_iter_ attribute of :class:`cluster.MiniBatchKMeans` now reports the number of started epochs and the n_steps_ attribute reports the number of mini batches processed. :pr:`17622` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| :func:`cluster.spectral_clustering` raises an improved error when passed a np.matrix. :pr:`20560` by `Thomas Fan`_.
- |Enhancement| :class:`compose.ColumnTransformer` now records the output of each transformer in output_indices_. :pr:`18393` by :user:`Luca Bittarello <lbittarello>`.
- |Enhancement| :class:`compose.ColumnTransformer` now allows DataFrame input to have its columns appear in a changed order in transform. Further, columns that are dropped will not be required in transform, and additional columns will be ignored if remainder='drop'. :pr:`19263` by `Thomas Fan`_.
- |Enhancement| Adds **predict_params keyword argument to :meth:`compose.TransformedTargetRegressor.predict` that passes keyword argument to the regressor. :pr:`19244` by :user:`Ricardo <ricardojnf>`.
- |FIX| compose.ColumnTransformer.get_feature_names supports
non-string feature names returned by any of its transformers. However, note
that
get_feature_namesis deprecated, useget_feature_names_outinstead. :pr:`18459` by :user:`Albert Villanova del Moral <albertvillanova>` and :user:`Alonso Silva Allende <alonsosilvaallende>`. - |Fix| :class:`compose.TransformedTargetRegressor` now takes nD targets with an adequate transformer. :pr:`18898` by :user:`Oras Phongpanagnam <panangam>`.
- |API| Adds verbose_feature_names_out to :class:`compose.ColumnTransformer`. This flag controls the prefixing of feature names out in :term:`get_feature_names_out`. :pr:`18444` and :pr:`21080` by `Thomas Fan`_.
- |Fix| Adds arrays check to :func:`covariance.ledoit_wolf` and :func:`covariance.ledoit_wolf_shrinkage`. :pr:`20416` by :user:`Hugo Defois <defoishugo>`.
- |API| Deprecates the following keys in cv_results_: 'mean_score', 'std_score', and 'split(k)_score' in favor of 'mean_test_score' 'std_test_score', and 'split(k)_test_score'. :pr:`20583` by `Thomas Fan`_.
- |Enhancement| :func:`datasets.fetch_openml` now supports categories with missing values when returning a pandas dataframe. :pr:`19365` by `Thomas Fan`_ and :user:`Amanda Dsouza <amy12xx>` and :user:`EL-ATEIF Sara <elateifsara>`.
- |Enhancement| :func:`datasets.fetch_kddcup99` raises a better message when the cached file is invalid. :pr:`19669` `Thomas Fan`_.
- |Enhancement| Replace usages of
__file__related to resource file I/O withimportlib.resourcesto avoid the assumption that these resource files (e.g.iris.csv) already exist on a filesystem, and by extension to enable compatibility with tools such asPyOxidizer. :pr:`20297` by :user:`Jack Liu <jackzyliu>`. - |Fix| Shorten data file names in the openml tests to better support installing on Windows and its default 260 character limit on file names. :pr:`20209` by `Thomas Fan`_.
- |Fix| :func:`datasets.fetch_kddcup99` returns dataframes when return_X_y=True and as_frame=True. :pr:`19011` by `Thomas Fan`_.
- |API| Deprecates :func:`datasets.load_boston` in 1.0 and it will be removed in 1.2. Alternative code snippets to load similar datasets are provided. Please report to the docstring of the function for details. :pr:`20729` by `Guillaume Lemaitre`_.
- |Enhancement| added a new approximate solver (randomized SVD, available with eigen_solver='randomized') to :class:`decomposition.KernelPCA`. This significantly accelerates computation when the number of samples is much larger than the desired number of components. :pr:`12069` by :user:`Sylvain Marié <smarie>`.
- |Fix| Fixes incorrect multiple data-conversion warnings when clustering boolean data. :pr:`19046` by :user:`Surya Prakash <jdsurya>`.
- |Fix| Fixed :func:`dict_learning`, used by :class:`decomposition.DictionaryLearning`, to ensure determinism of the output. Achieved by flipping signs of the SVD output which is used to initialize the code. :pr:`18433` by :user:`Bruno Charron <brcharron>`.
- |Fix| Fixed a bug in :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.MiniBatchSparsePCA` and :func:`decomposition.dict_learning_online` where the update of the dictionary was incorrect. :pr:`19198` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixed a bug in :class:`decomposition.DictionaryLearning`, :class:`decomposition.SparsePCA`, :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.MiniBatchSparsePCA`, :func:`decomposition.dict_learning` and :func:`decomposition.dict_learning_online` where the restart of unused atoms during the dictionary update was not working as expected. :pr:`19198` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| In :class:`decomposition.DictionaryLearning`, :class:`decomposition.MiniBatchDictionaryLearning`, :func:`decomposition.dict_learning` and :func:`decomposition.dict_learning_online`, transform_alpha will be equal to alpha instead of 1.0 by default starting from version 1.2 :pr:`19159` by :user:`Benoît Malézieux <bmalezieux>`.
- |API| Rename variable names in :class:`KernelPCA` to improve readability. lambdas_ and alphas_ are renamed to eigenvalues_ and eigenvectors_, respectively. lambdas_ and alphas_ are deprecated and will be removed in 1.2. :pr:`19908` by :user:`Kei Ishikawa <kstoneriv3>`.
- |API| The alpha and regularization parameters of :class:`decomposition.NMF` and :func:`decomposition.non_negative_factorization` are deprecated and will be removed in 1.2. Use the new parameters alpha_W and alpha_H instead. :pr:`20512` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| Attribute n_features_in_ in :class:`dummy.DummyRegressor` and :class:`dummy.DummyRegressor` is deprecated and will be removed in 1.2. :pr:`20960` by `Thomas Fan`_.
- |Enhancement| :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor` take cgroups quotas into account when deciding the number of threads used by OpenMP. This avoids performance problems caused by over-subscription when using those classes in a docker container for instance. :pr:`20477` by `Thomas Fan`_.
- |Enhancement| :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor` are no longer experimental. They are now considered stable and are subject to the same deprecation cycles as all other estimators. :pr:`19799` by `Nicolas Hug`_.
- |Enhancement| Improve the HTML rendering of the :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`. :pr:`19564` by `Thomas Fan`_.
- |Enhancement| Added Poisson criterion to :class:`ensemble.RandomForestRegressor`. :pr:`19836` by :user:`Brian Sun <bsun94>`.
- |Fix| Do not allow to compute out-of-bag (OOB) score in :class:`ensemble.RandomForestClassifier` and :class:`ensemble.ExtraTreesClassifier` with multiclass-multioutput target since scikit-learn does not provide any metric supporting this type of target. Additional private refactoring was performed. :pr:`19162` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| Improve numerical precision for weights boosting in :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` to avoid underflows. :pr:`10096` by :user:`Fenil Suchak <fenilsuchak>`.
- |Fix| Fixed the range of the argument
max_samplesto be(0.0, 1.0]in :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, where max_samples=1.0 is interpreted as using all n_samples for bootstrapping. :pr:`20159` by :user:`murata-yu`. - |Fix| Fixed a bug in :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` where the sample_weight parameter got overwritten during fit. :pr:`20534` by :user:`Guillaume Lemaitre <glemaitre>`.
- |API| Removes tol=None option in :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. Please use tol=0 for the same behavior. :pr:`19296` by `Thomas Fan`_.
- |Fix| Fixed a bug in :class:`feature_extraction.text.HashingVectorizer` where some input strings would result in negative indices in the transformed data. :pr:`19035` by :user:`Liu Yu <ly648499246>`.
- |Fix| Fixed a bug in :class:`feature_extraction.DictVectorizer` by raising an error with unsupported value type. :pr:`19520` by :user:`Jeff Zhao <kamiyaa>`.
- |Fix| Fixed a bug in :func:`feature_extraction.image.img_to_graph` and :func:`feature_extraction.image.grid_to_graph` where singleton connected components were not handled properly, resulting in a wrong vertex indexing. :pr:`18964` by `Bertrand Thirion`_.
- |Fix| Raise a warning in :class:`feature_extraction.text.CountVectorizer` with lowercase=True when there are vocabulary entries with uppercase characters to avoid silent misses in the resulting feature vectors. :pr:`19401` by :user:`Zito Relova <zitorelova>`
- |Feature| :func:`feature_selection.r_regression` computes Pearson's R correlation coefficients between the features and the target. :pr:`17169` by :user:`Dmytro Lituiev <DSLituiev>` and :user:`Julien Jerphanion <jjerphan>`.
- |Enhancement| :func:`feature_selection.RFE.fit` accepts additional estimator parameters that are passed directly to the estimator's fit method. :pr:`20380` by :user:`Iván Pulido <ijpulidos>`, :user:`Felipe Bidu <fbidu>`, :user:`Gil Rutter <g-rutter>`, and :user:`Adrin Jalali <adrinjalali>`.
- |FIX| Fix a bug in :func:`isotonic.isotonic_regression` where the
sample_weight passed by a user were overwritten during
fit. :pr:`20515` by :user:`Carsten Allefeld <allefeld>`. - |Fix| Change :func:`feature_selection.SequentialFeatureSelector` to allow for unsupervised modelling so that the fit signature need not do any y validation and allow for y=None. :pr:`19568` by :user:`Shyam Desai <ShyamDesai>`.
- |API| Raises an error in :class:`feature_selection.VarianceThreshold` when the variance threshold is negative. :pr:`20207` by :user:`Tomohiro Endo <europeanplaice>`
- |API| Deprecates grid_scores_ in favor of split scores in cv_results_ in :class:`feature_selection.RFECV`. grid_scores_ will be removed in version 1.2. :pr:`20161` by :user:`Shuhei Kayawari <wowry>` and :user:`arka204`.
- |Enhancement| Add max_samples parameter in :func:`inspection.permutation_importance`. It enables to draw a subset of the samples to compute the permutation importance. This is useful to keep the method tractable when evaluating feature importance on large datasets. :pr:`20431` by :user:`Oliver Pfaffel <o1iv3r>`.
- |Enhancement| Add kwargs to format ICE and PD lines separately in partial dependence plots :func:`inspection.plot_partial_dependence` and :meth:`inspection.PartialDependenceDisplay.plot`. :pr:`19428` by :user:`Mehdi Hamoumi <mhham>`.
- |Fix| Allow multiple scorers input to :func:`inspection.permutation_importance`. :pr:`19411` by :user:`Simona Maggio <simonamaggio>`.
- |API| :class:`inspection.PartialDependenceDisplay` exposes a class method: :func:`~inspection.PartialDependenceDisplay.from_estimator`. :func:`inspection.plot_partial_dependence` is deprecated in favor of the class method and will be removed in 1.2. :pr:`20959` by `Thomas Fan`_.
- |Fix| Fix a bug in :class:`kernel_approximation.Nystroem` where the attribute component_indices_ did not correspond to the subset of sample indices used to generate the approximated kernel. :pr:`20554` by :user:`Xiangyin Kong <kxytim>`.
- |MajorFeature| Added :class:`linear_model.QuantileRegressor` which implements linear quantile regression with L1 penalty. :pr:`9978` by :user:`David Dale <avidale>` and :user:`Christian Lorentzen <lorentzenchr>`.
- |Feature| The new :class:`linear_model.SGDOneClassSVM` provides an SGD implementation of the linear One-Class SVM. Combined with kernel approximation techniques, this implementation approximates the solution of a kernelized One Class SVM while benefitting from a linear complexity in the number of samples. :pr:`10027` by :user:`Albert Thomas <albertcthomas>`.
- |Feature| Added sample_weight parameter to :class:`linear_model.LassoCV` and :class:`linear_model.ElasticNetCV`. :pr:`16449` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Feature| Added new solver lbfgs (available with solver="lbfgs") and positive argument to :class:`linear_model.Ridge`. When positive is set to True, forces the coefficients to be positive (only supported by lbfgs). :pr:`20231` by :user:`Toshihiro Nakae <tnakae>`.
- |Efficiency| The implementation of :class:`linear_model.LogisticRegression` has been optimised for dense matrices when using solver='newton-cg' and multi_class!='multinomial'. :pr:`19571` by :user:`Julien Jerphanion <jjerphan>`.
- |Enhancement| fit method preserves dtype for numpy.float32 in :class:`linear_model.Lars`, :class:`linear_model.LassoLars`, :class:`linear_model.LassoLars`, :class:`linear_model.LarsCV` and :class:`linear_model.LassoLarsCV`. :pr:`20155` by :user:`Takeshi Oura <takoika>`.
- |Enhancement| Validate user-supplied gram matrix passed to linear models via the precompute argument. :pr:`19004` by :user:`Adam Midvidy <amidvidy>`.
- |Fix| :meth:`linear_model.ElasticNet.fit` no longer modifies sample_weight in place. :pr:`19055` by `Thomas Fan`_.
- |Fix| :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet` no longer have a dual_gap_ not corresponding to their objective. :pr:`19172` by :user:`Mathurin Massias <mathurinm>`
- |Fix| sample_weight are now fully taken into account in linear models when normalize=True for both feature centering and feature scaling. :pr:`19426` by :user:`Alexandre Gramfort <agramfort>` and :user:`Maria Telenczuk <maikia>`.
- |Fix| Points with residuals equal to
residual_thresholdare now considered as inliers for :class:`linear_model.RANSACRegressor`. This allows fitting a model perfectly on some datasets when residual_threshold=0. :pr:`19499` by :user:`Gregory Strubel <gregorystrubel>`. - |Fix| Sample weight invariance for :class:`linear_model.Ridge` was fixed in :pr:`19616` by :user:`Oliver Grisel <ogrisel>` and :user:`Christian Lorentzen <lorentzenchr>`.
- |Fix| The dictionary params in :func:`linear_model.enet_path` and :func:`linear_model.lasso_path` should only contain parameter of the coordinate descent solver. Otherwise, an error will be raised. :pr:`19391` by :user:`Shao Yang Hong <hongshaoyang>`.
- |API| Raise a warning in :class:`linear_model.RANSACRegressor` that from version 1.2, min_samples need to be set explicitly for models other than :class:`linear_model.LinearRegression`. :pr:`19390` by :user:`Shao Yang Hong <hongshaoyang>`.
- |API|: The parameter
normalizeof :class:`linear_model.LinearRegression` is deprecated and will be removed in 1.2. Motivation for this deprecation:normalizeparameter did not take any effect iffit_interceptwas set to False and therefore was deemed confusing. The behavior of the deprecatedLinearModel(normalize=True)can be reproduced with a :class:`~sklearn.pipeline.Pipeline` withLinearModel(whereLinearModelis :class:`~linear_model.LinearRegression`, :class:`~linear_model.Ridge`, :class:`~linear_model.RidgeClassifier`, :class:`~linear_model.RidgeCV` or :class:`~linear_model.RidgeClassifierCV`) as follows:make_pipeline(StandardScaler(with_mean=False), LinearModel()). Thenormalizeparameter in :class:`~linear_model.LinearRegression` was deprecated in :pr:`17743` by :user:`Maria Telenczuk <maikia>` and :user:`Alexandre Gramfort <agramfort>`. Same for :class:`~linear_model.Ridge`, :class:`~linear_model.RidgeClassifier`, :class:`~linear_model.RidgeCV`, and :class:`~linear_model.RidgeClassifierCV`, in: :pr:`17772` by :user:`Maria Telenczuk <maikia>` and :user:`Alexandre Gramfort <agramfort>`. Same for :class:`~linear_model.BayesianRidge`, :class:`~linear_model.ARDRegression` in: :pr:`17746` by :user:`Maria Telenczuk <maikia>`. Same for :class:`~linear_model.Lasso`, :class:`~linear_model.LassoCV`, :class:`~linear_model.ElasticNet`, :class:`~linear_model.ElasticNetCV`, :class:`~linear_model.MultiTaskLasso`, :class:`~linear_model.MultiTaskLassoCV`, :class:`~linear_model.MultiTaskElasticNet`, :class:`~linear_model.MultiTaskElasticNetCV`, in: :pr:`17785` by :user:`Maria Telenczuk <maikia>` and :user:`Alexandre Gramfort <agramfort>`. - |API| The
normalizeparameter of :class:`~linear_model.OrthogonalMatchingPursuit` and :class:`~linear_model.OrthogonalMatchingPursuitCV` will default to False in 1.2 and will be removed in 1.4. :pr:`17750` by :user:`Maria Telenczuk <maikia>` and :user:`Alexandre Gramfort <agramfort>`. Same for :class:`~linear_model.Lars` :class:`~linear_model.LarsCV` :class:`~linear_model.LassoLars` :class:`~linear_model.LassoLarsCV` :class:`~linear_model.LassoLarsIC`, in :pr:`17769` by :user:`Maria Telenczuk <maikia>` and :user:`Alexandre Gramfort <agramfort>`. - |API| Keyword validation has moved from __init__ and set_params to fit for the following estimators conforming to scikit-learn's conventions: :class:`~linear_model.SGDClassifier`, :class:`~linear_model.SGDRegressor`, :class:`~linear_model.SGDOneClassSVM`, :class:`~linear_model.PassiveAggressiveClassifier`, and :class:`~linear_model.PassiveAggressiveRegressor`. :pr:`20683` by `Guillaume Lemaitre`_.
- |Enhancement| Implement 'auto' heuristic for the learning_rate in :class:`manifold.TSNE`. It will become default in 1.2. The default initialization will change to pca in 1.2. PCA initialization will be scaled to have standard deviation 1e-4 in 1.2. :pr:`19491` by :user:`Dmitry Kobak <dkobak>`.
- |Fix| Change numerical precision to prevent underflow issues during affinity matrix computation for :class:`manifold.TSNE`. :pr:`19472` by :user:`Dmitry Kobak <dkobak>`.
- |Fix| :class:`manifold.Isomap` now uses scipy.sparse.csgraph.shortest_path to compute the graph shortest path. It also connects disconnected components of the neighbors graph along some minimum distance pairs, instead of changing every infinite distances to zero. :pr:`20531` by `Roman Yurchak`_ and `Tom Dupre la Tour`_.
- |Fix| Decrease the numerical default tolerance in the lobpcg call in :func:`manifold.spectral_embedding` to prevent numerical instability. :pr:`21194` by :user:`Andrew Knyazev <lobpcg>`.
- |Feature| :func:`metrics.mean_pinball_loss` exposes the pinball loss for quantile regression. :pr:`19415` by :user:`Xavier Dupré <sdpython>` and :user:`Oliver Grisel <ogrisel>`.
- |Feature| :func:`metrics.d2_tweedie_score` calculates the D^2 regression
score for Tweedie deviances with power parameter
power. This is a generalization of the r2_score and can be interpreted as percentage of Tweedie deviance explained. :pr:`17036` by :user:`Christian Lorentzen <lorentzenchr>`. - |Feature| :func:`metrics.mean_squared_log_error` now supports squared=False. :pr:`20326` by :user:`Uttam kumar <helper-uttam>`.
- |Efficiency| Improved speed of :func:`metrics.confusion_matrix` when labels are integral. :pr:`9843` by :user:`Jon Crall <Erotemic>`.
- |Enhancement| A fix to raise an error in :func:`metrics.hinge_loss` when
pred_decisionis 1d whereas it is a multiclass classification or whenpred_decisionparameter is not consistent with thelabelsparameter. :pr:`19643` by :user:`Pierre Attard <PierreAttard>`. - |Fix| :meth:`metrics.ConfusionMatrixDisplay.plot` uses the correct max for colormap. :pr:`19784` by `Thomas Fan`_.
- |Fix| Samples with zero sample_weight values do not affect the results from :func:`metrics.det_curve`, :func:`metrics.precision_recall_curve` and :func:`metrics.roc_curve`. :pr:`18328` by :user:`Albert Villanova del Moral <albertvillanova>` and :user:`Alonso Silva Allende <alonsosilvaallende>`.
- |Fix| avoid overflow in :func:`metrics.cluster.adjusted_rand_score` with large amount of data. :pr:`20312` by :user:`Divyanshu Deoli <divyanshudeoli>`.
- |API| :class:`metrics.ConfusionMatrixDisplay` exposes two class methods :func:`~metrics.ConfusionMatrixDisplay.from_estimator` and :func:`~metrics.ConfusionMatrixDisplay.from_predictions` allowing to create a confusion matrix plot using an estimator or the predictions. :func:`metrics.plot_confusion_matrix` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`18543` by `Guillaume Lemaitre`_.
- |API| :class:`metrics.PrecisionRecallDisplay` exposes two class methods :func:`~metrics.PrecisionRecallDisplay.from_estimator` and :func:`~metrics.PrecisionRecallDisplay.from_predictions` allowing to create a precision-recall curve using an estimator or the predictions. :func:`metrics.plot_precision_recall_curve` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`20552` by `Guillaume Lemaitre`_.
- |API| :class:`metrics.DetCurveDisplay` exposes two class methods :func:`~metrics.DetCurveDisplay.from_estimator` and :func:`~metrics.DetCurveDisplay.from_predictions` allowing to create a confusion matrix plot using an estimator or the predictions. :func:`metrics.plot_det_curve` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`19278` by `Guillaume Lemaitre`_.
- |Fix| Ensure that the best parameters are set appropriately in the case of divergency for :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`. :pr:`20030` by :user:`Tingshan Liu <tliu68>` and :user:`Benjamin Pedigo <bdpedigo>`.
- |Feature| added :class:`model_selection.StratifiedGroupKFold`, that combines :class:`model_selection.StratifiedKFold` and :class:`model_selection.GroupKFold`, providing an ability to split data preserving the distribution of classes in each split while keeping each group within a single split. :pr:`18649` by :user:`Leandro Hermida <hermidalc>` and :user:`Rodion Martynov <marrodion>`.
- |Enhancement| warn only once in the main process for per-split fit failures in cross-validation. :pr:`20619` by :user:`Loïc Estève <lesteve>`
- |Enhancement| The :class:`model_selection.BaseShuffleSplit` base class is now public. :pr:`20056` by :user:`pabloduque0`.
- |Fix| Avoid premature overflow in :func:`model_selection.train_test_split`. :pr:`20904` by :user:`Tomasz Jakubek <t-jakubek>`.
- |Fix| The fit and partial_fit methods of the discrete naive Bayes classifiers (:class:`naive_bayes.BernoulliNB`, :class:`naive_bayes.CategoricalNB`, :class:`naive_bayes.ComplementNB`, and :class:`naive_bayes.MultinomialNB`) now correctly handle the degenerate case of a single class in the training set. :pr:`18925` by :user:`David Poznik <dpoznik>`.
- |API| The attribute
sigma_is now deprecated in :class:`naive_bayes.GaussianNB` and will be removed in 1.2. Usevar_instead. :pr:`18842` by :user:`Hong Shao Yang <hongshaoyang>`.
- |Enhancement| The creation of :class:`neighbors.KDTree` and :class:`neighbors.BallTree` has been improved for their worst-cases time complexity from \mathcal{O}(n^2) to \mathcal{O}(n). :pr:`19473` by :user:`jiefangxuanyan <jiefangxuanyan>` and :user:`Julien Jerphanion <jjerphan>`.
- |FIX| :class:`neighbors.DistanceMetric` subclasses now support readonly memory-mapped datasets. :pr:`19883` by :user:`Julien Jerphanion <jjerphan>`.
- |FIX| :class:`neighbors.NearestNeighbors`, :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor` and :class:`neighbors.RadiusNeighborsRegressor` do not validate weights in __init__ and validates weights in fit instead. :pr:`20072` by :user:`Juan Carlos Alfaro Jiménez <alfaro96>`.
- |API| The parameter kwargs of :class:`neighbors.RadiusNeighborsClassifier` is deprecated and will be removed in 1.2. :pr:`20842` by :user:`Juan Martín Loyola <jmloyola>`.
- |Fix| :class:`neural_network.MLPClassifier` and :class:`neural_network.MLPRegressor` now correctly support continued training when loading from a pickled file. :pr:`19631` by `Thomas Fan`_.
- |API| The predict_proba and predict_log_proba methods of the :class:`pipeline.Pipeline` now support passing prediction kwargs to the final estimator. :pr:`19790` by :user:`Christopher Flynn <crflynn>`.
- |Feature| The new :class:`preprocessing.SplineTransformer` is a feature
preprocessing tool for the generation of B-splines, parametrized by the
polynomial
degreeof the splines, number of knotsn_knotsand knot positioning strategyknots. :pr:`18368` by :user:`Christian Lorentzen <lorentzenchr>`. :class:`preprocessing.SplineTransformer` also supports periodic splines via theextrapolationargument. :pr:`19483` by :user:`Malte Londschien <mlondschien>`. :class:`preprocessing.SplineTransformer` supports sample weights for knot position strategy"quantile". :pr:`20526` by :user:`Malte Londschien <mlondschien>`. - |Feature| :class:`preprocessing.OrdinalEncoder` supports passing through missing values by default. :pr:`19069` by `Thomas Fan`_.
- |Feature| :class:`preprocessing.OneHotEncoder` now supports handle_unknown='ignore' and dropping categories. :pr:`19041` by `Thomas Fan`_.
- |Feature| :class:`preprocessing.PolynomialFeatures` now supports passing a tuple to degree, i.e. degree=(min_degree, max_degree). :pr:`20250` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Efficiency| :class:`preprocessing.StandardScaler` is faster and more memory efficient. :pr:`20652` by `Thomas Fan`_.
- |Efficiency| Changed
algorithmargument for :class:`cluster.KMeans` in :class:`preprocessing.KBinsDiscretizer` fromautotofull. :pr:`19934` by :user:`Gleb Levitskiy <GLevV>`. - |Efficiency| The implementation of fit for :class:`preprocessing.PolynomialFeatures` transformer is now faster. This is especially noticeable on large sparse input. :pr:`19734` by :user:`Fred Robinson <frrad>`.
- |Fix| The :func:`preprocessing.StandardScaler.inverse_transform` method now raises error when the input data is 1D. :pr:`19752` by :user:`Zhehao Liu <Max1993Liu>`.
- |Fix| :func:`preprocessing.scale`, :class:`preprocessing.StandardScaler` and similar scalers detect near-constant features to avoid scaling them to very large values. This problem happens in particular when using a scaler on sparse data with a constant column with sample weights, in which case centering is typically disabled. :pr:`19527` by :user:`Oliver Grisel <ogrisel>` and :user:`Maria Telenczuk <maikia>` and :pr:`19788` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| :meth:`preprocessing.StandardScaler.inverse_transform` now correctly handles integer dtypes. :pr:`19356` by :user:`makoeppel`.
- |Fix| :meth:`preprocessing.OrdinalEncoder.inverse_transform` is not supporting sparse matrix and raises the appropriate error message. :pr:`19879` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| The fit method of :class:`preprocessing.OrdinalEncoder` will not raise error when handle_unknown='ignore' and unknown categories are given to fit. :pr:`19906` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Fix| Fix a regression in :class:`preprocessing.OrdinalEncoder` where large Python numeric would raise an error due to overflow when casted to C type (np.float64 or np.int64). :pr:`20727` by `Guillaume Lemaitre`_.
- |Fix| :class:`preprocessing.FunctionTransformer` does not set n_features_in_ based on the input to inverse_transform. :pr:`20961` by `Thomas Fan`_.
- |API| The n_input_features_ attribute of :class:`preprocessing.PolynomialFeatures` is deprecated in favor of n_features_in_ and will be removed in 1.2. :pr:`20240` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| The parameter **params of :func:`svm.OneClassSVM.fit` is deprecated and will be removed in 1.2. :pr:`20843` by :user:`Juan Martín Loyola <jmloyola>`.
- |Enhancement| Add fontname argument in :func:`tree.export_graphviz` for non-English characters. :pr:`18959` by :user:`Zero <Zeroto521>` and :user:`wstates <wstates>`.
- |Fix| Improves compatibility of :func:`tree.plot_tree` with high DPI screens. :pr:`20023` by `Thomas Fan`_.
- |Fix| Fixed a bug in :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor` where a node could be split whereas it should not have been due to incorrect handling of rounding errors. :pr:`19336` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| The n_features_ attribute of :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor` is deprecated in favor of n_features_in_ and will be removed in 1.2. :pr:`20272` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Enhancement| Deprecated the default value of the random_state=0 in :func:`~sklearn.utils.extmath.randomized_svd`. Starting in 1.2, the default value of random_state will be set to None. :pr:`19459` by :user:`Cindy Bezuidenhout <cinbez>` and :user:`Clifford Akai-Nettey<cliffordEmmanuel>`.
- |Enhancement| Added helper decorator :func:`utils.metaestimators.available_if` to provide flexiblity in metaestimators making methods available or unavailable on the basis of state, in a more readable way. :pr:`19948` by `Joel Nothman`_.
- |Enhancement| :func:`utils.validation.check_is_fitted` now uses
__sklearn_is_fitted__if available, instead of checking for attributes ending with an underscore. This also makes :class:`pipeline.Pipeline` and :class:`preprocessing.FunctionTransformer` passcheck_is_fitted(estimator). :pr:`20657` by `Adrin Jalali`_. - |Fix| Fixed a bug in :func:`utils.sparsefuncs.mean_variance_axis` where the precision of the computed variance was very poor when the real variance is exactly zero. :pr:`19766` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| The docstrings of propreties that are decorated with :func:`utils.deprecated` are now properly wrapped. :pr:`20385` by `Thomas Fan`_.
- |Fix| :func:`utils.stats._weighted_percentile` now correctly ignores
zero-weighted observations smaller than the smallest observation with
positive weight for
percentile=0. Affected classes are :class:`dummy.DummyRegressor` forquantile=0and :class:`ensemble.HuberLossFunction` and :class:`ensemble.HuberLossFunction` foralpha=0. :pr:`20528` by :user:`Malte Londschien <mlondschien>`. - |Fix| :func:`utils._safe_indexing` explicitly takes a dataframe copy when integer indices are provided avoiding to raise a warning from Pandas. This warning was previously raised in resampling utilities and functions using those utilities (e.g. :func:`model_selection.train_test_split`, :func:`model_selection.cross_validate`, :func:`model_selection.cross_val_score`, :func:`model_selection.cross_val_predict`). :pr:`20673` by :user:`Joris Van den Bossche <jorisvandenbossche>`.
- |Fix| Fix a regression in :func:`utils.is_scalar_nan` where large Python numbers would raise an error due to overflow in C types (np.float64 or np.int64). :pr:`20727` by `Guillaume Lemaitre`_.
- |Fix| Support for np.matrix is deprecated in :func:`~sklearn.utils.check_array` in 1.0 and will raise a TypeError in 1.2. :pr:`20165` by `Thomas Fan`_.
- |API| utils._testing.assert_warns and utils._testing.assert_warns_message are deprecated in 1.0 and will be removed in 1.2. Used pytest.warns context manager instead. Note that these functions were not documented and part from the public API. :pr:`20521` by :user:`Olivier Grisel <ogrisel>`.
- |API| Fixed several bugs in :func:`utils.graph.graph_shortest_path`, which is now deprecated. Use scipy.sparse.csgraph.shortest_path instead. :pr:`20531` by `Tom Dupre la Tour`_.
Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.24, including:
Abdulelah S. Al Mesfer, Abhinav Gupta, Adam J. Stewart, Adam Li, Adam Midvidy, Adrian Garcia Badaracco, Adrian Sadłocha, Adrin Jalali, Agamemnon Krasoulis, Alberto Rubiales, Albert Thomas, Albert Villanova del Moral, Alek Lefebvre, Alessia Marcolini, Alexandr Fonari, Alihan Zihna, Aline Ribeiro de Almeida, Amanda, Amanda Dsouza, Amol Deshmukh, Ana Pessoa, Anavelyz, Andreas Mueller, Andrew Delong, Ashish, Ashvith Shetty, Atsushi Nukariya, Aurélien Geron, Avi Gupta, Ayush Singh, baam, BaptBillard, Benjamin Pedigo, Bertrand Thirion, Bharat Raghunathan, bmalezieux, Brian Rice, Brian Sun, Bruno Charron, Bryan Chen, bumblebee, caherrera-meli, Carsten Allefeld, CeeThinwa, Chiara Marmo, chrissobel, Christian Lorentzen, Christopher Yeh, Chuliang Xiao, Clément Fauchereau, cliffordEmmanuel, Conner Shen, Connor Tann, David Dale, David Katz, David Poznik, Dimitri Papadopoulos Orfanos, Divyanshu Deoli, dmallia17, Dmitry Kobak, DS_anas, Eduardo Jardim, EdwinWenink, EL-ATEIF Sara, Eleni Markou, EricEllwanger, Eric Fiegel, Erich Schubert, Ezri-Mudde, Fatos Morina, Felipe Rodrigues, Felix Hafner, Fenil Suchak, flyingdutchman23, Flynn, Fortune Uwha, Francois Berenger, Frankie Robertson, Frans Larsson, Frederick Robinson, frellwan, Gabriel S Vicente, Gael Varoquaux, genvalen, Geoffrey Thomas, geroldcsendes, Gleb Levitskiy, Glen, Glòria Macià Muñoz, gregorystrubel, groceryheist, Guillaume Lemaitre, guiweber, Haidar Almubarak, Hans Moritz Günther, Haoyin Xu, Harris Mirza, Harry Wei, Harutaka Kawamura, Hassan Alsawadi, Helder Geovane Gomes de Lima, Hugo DEFOIS, Igor Ilic, Ikko Ashimine, Isaack Mungui, Ishaan Bhat, Ishan Mishra, Iván Pulido, iwhalvic, J Alexander, Jack Liu, James Alan Preiss, James Budarz, James Lamb, Jannik, Jeff Zhao, Jennifer Maldonado, Jérémie du Boisberranger, Jesse Lima, Jianzhu Guo, jnboehm, Joel Nothman, JohanWork, John Paton, Jonathan Schneider, Jon Crall, Jon Haitz Legarreta Gorroño, Joris Van den Bossche, José Manuel Nápoles Duarte, Juan Carlos Alfaro Jiménez, Juan Martin Loyola, Julien Jerphanion, Julio Batista Silva, julyrashchenko, JVM, Kadatatlu Kishore, Karen Palacio, Kei Ishikawa, kmatt10, kobaski, Kot271828, Kunj, KurumeYuta, kxytim, lacrosse91, LalliAcqua, Laveen Bagai, Leonardo Rocco, Leonardo Uieda, Leopoldo Corona, Loic Esteve, LSturtew, Luca Bittarello, Luccas Quadros, Lucy Jiménez, Lucy Liu, ly648499246, Mabu Manaileng, Manimaran, makoeppel, Marco Gorelli, Maren Westermann, Mariangela, Maria Telenczuk, marielaraj, Martin Hirzel, Mateo Noreña, Mathieu Blondel, Mathis Batoul, mathurinm, Matthew Calcote, Maxime Prieur, Maxwell, Mehdi Hamoumi, Mehmet Ali Özer, Miao Cai, Michal Karbownik, michalkrawczyk, Mitzi, mlondschien, Mohamed Haseeb, Mohamed Khoualed, Muhammad Jarir Kanji, murata-yu, Nadim Kawwa, Nanshan Li, naozin555, Nate Parsons, Neal Fultz, Nic Annau, Nicolas Hug, Nicolas Miller, Nico Stefani, Nigel Bosch, Nikita Titov, Nodar Okroshiashvili, Norbert Preining, novaya, Ogbonna Chibuike Stephen, OGordon100, Oliver Pfaffel, Olivier Grisel, Oras Phongpanangam, Pablo Duque, Pablo Ibieta-Jimenez, Patric Lacouth, Paulo S. Costa, Paweł Olszewski, Peter Dye, PierreAttard, Pierre-Yves Le Borgne, PranayAnchuri, Prince Canuma, putschblos, qdeffense, RamyaNP, ranjanikrishnan, Ray Bell, Rene Jean Corneille, Reshama Shaikh, ricardojnf, RichardScottOZ, Rodion Martynov, Rohan Paul, Roman Lutz, Roman Yurchak, Samuel Brice, Sandy Khosasi, Sean Benhur J, Sebastian Flores, Sebastian Pölsterl, Shao Yang Hong, shinehide, shinnar, shivamgargsya, Shooter23, Shuhei Kayawari, Shyam Desai, simonamaggio, Sina Tootoonian, solosilence, Steven Kolawole, Steve Stagg, Surya Prakash, swpease, Sylvain Marié, Takeshi Oura, Terence Honles, TFiFiE, Thomas A Caswell, Thomas J. Fan, Tim Gates, TimotheeMathieu, Timothy Wolodzko, Tim Vink, t-jakubek, t-kusanagi, tliu68, Tobias Uhmann, tom1092, Tomás Moreyra, Tomás Ronald Hughes, Tom Dupré la Tour, Tommaso Di Noto, Tomohiro Endo, TONY GEORGE, Toshihiro NAKAE, tsuga, Uttam kumar, vadim-ushtanit, Vangelis Gkiastas, Venkatachalam N, Vilém Zouhar, Vinicius Rios Fuck, Vlasovets, waijean, Whidou, xavier dupré, xiaoyuchai, Yasmeen Alsaedy, yoch, Yosuke KOBAYASHI, Yu Feng, YusukeNagasaka, yzhenman, Zero, ZeyuSun, ZhaoweiWang, Zito, Zito Relova