.. currentmodule:: sklearn
May 2022
- |Enhancement| The error message is improved when importing :class:`model_selection.HalvingGridSearchCV`, :class:`model_selection.HalvingRandomSearchCV`, or :class:`impute.IterativeImputer` without importing the experimental flag. :pr:`23194` by `Thomas Fan`_.
- |Enhancement| Added an extension in doc/conf.py to automatically generate the list of estimators that handle NaN values. :pr:`23198` by :user:`Lise Kleiber <lisekleiber>`, :user:`Zhehao Liu <MaxwellLZH>` and :user:`Chiara Marmo <cmarmo>`.
- |Fix| Avoid timeouts in :func:`datasets.fetch_openml` by not passing a timeout argument, :pr:`23358` by :user:`Loïc Estève <lesteve>`.
- |Fix| Avoid spurious warning in :class:`decomposition.IncrementalPCA` when n_samples == n_components. :pr:`23264` by :user:`Lucy Liu <lucyleeow>`.
- |Fix| The partial_fit method of :class:`feature_selection.SelectFromModel` now conducts validation for max_features and feature_names_in parameters. :pr:`23299` by :user:`Long Bao <lorentzbao>`.
- |Fix| Fixes :func:`metrics.precision_recall_curve` to compute precision-recall at 100% recall. The Precision-Recall curve now displays the last point corresponding to a classifier that always predicts the positive class: recall=100% and precision=class balance. :pr:`23214` by :user:`Stéphane Collot <stephanecollot>` and :user:`Max Baak <mbaak>`.
- |Fix| :class:`preprocessing.PolynomialFeatures` with
degreeequal to 0 will raise error wheninclude_biasis set to False, and outputs a single constant array wheninclude_biasis set to True. :pr:`23370` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Fix| Fixes performance regression with low cardinality features for :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.GradientBoostingClassifier`, and :class:`ensemble.GradientBoostingRegressor`. :pr:`23410` by :user:`Loïc Estève <lesteve>`.
- |Fix| :func:`utils.class_weight.compute_sample_weight` now works with sparse y. :pr:`23115` by :user:`kernc <kernc>`.
May 2022
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_1_0.py`.
Version 1.1.0 of scikit-learn requires python 3.8+, numpy 1.17.3+ and scipy 1.3.2+. Optional minimal dependency is matplotlib 3.1.2+.
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.
- |Efficiency| :class:`cluster.KMeans` now defaults to
algorithm="lloyd"instead ofalgorithm="auto", which was equivalent toalgorithm="elkan". Lloyd's algorithm and Elkan's algorithm converge to the same solution, up to numerical rounding errors, but in general Lloyd's algorithm uses much less memory, and it is often faster. - |Efficiency| Fitting :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.GradientBoostingClassifier`, and :class:`ensemble.GradientBoostingRegressor` is on average 15% faster than in previous versions thanks to a new sort algorithm to find the best split. Models might be different because of a different handling of splits with tied criterion values: both the old and the new sorting algorithm are unstable sorting algorithms. :pr:`22868` by `Thomas Fan`_.
- |Fix| The eigenvectors initialization for :class:`cluster.SpectralClustering` and :class:`manifold.SpectralEmbedding` now samples from a Gaussian when using the 'amg' or 'lobpcg' solver. This change improves numerical stability of the solver, but may result in a different model.
- |Fix| :func:`feature_selection.f_regression` and :func:`feature_selection.r_regression` will now returned finite score by default instead of np.nan and np.inf for some corner case. You can use force_finite=False if you really want to get non-finite values and keep the old behavior.
- |Fix| Panda's DataFrames with all non-string columns such as a MultiIndex no longer warns when passed into an Estimator. Estimators will continue to ignore the column names in DataFrames with non-string columns. For feature_names_in_ to be defined, columns must be all strings. :pr:`22410` by `Thomas Fan`_.
- |Fix| :class:`preprocessing.KBinsDiscretizer` changed handling of bin edges slightly, which might result in a different encoding with the same data.
- |Fix| :func:`calibration.calibration_curve` changed handling of bin edges slightly, which might result in a different output curve given the same data.
- |Fix| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now uses the correct variance-scaling coefficient which may result in different model behavior.
- |Fix| :meth:`feature_selection.SelectFromModel.fit` and :meth:`feature_selection.SelectFromModel.partial_fit` can now be called with prefit=True. estimators_ will be a deep copy of estimator when prefit=True. :pr:`23271` by :user:`Guillaume Lemaitre <glemaitre>`.
|Efficiency| Low-level routines for reductions on pairwise distances for dense float64 datasets have been refactored. The following functions and estimators now benefit from improved performances in terms of hardware scalability and speed-ups:
- :func:`sklearn.metrics.pairwise_distances_argmin`
- :func:`sklearn.metrics.pairwise_distances_argmin_min`
- :class:`sklearn.cluster.AffinityPropagation`
- :class:`sklearn.cluster.Birch`
- :class:`sklearn.cluster.MeanShift`
- :class:`sklearn.cluster.OPTICS`
- :class:`sklearn.cluster.SpectralClustering`
- :func:`sklearn.feature_selection.mutual_info_regression`
- :class:`sklearn.neighbors.KNeighborsClassifier`
- :class:`sklearn.neighbors.KNeighborsRegressor`
- :class:`sklearn.neighbors.RadiusNeighborsClassifier`
- :class:`sklearn.neighbors.RadiusNeighborsRegressor`
- :class:`sklearn.neighbors.LocalOutlierFactor`
- :class:`sklearn.neighbors.NearestNeighbors`
- :class:`sklearn.manifold.Isomap`
- :class:`sklearn.manifold.LocallyLinearEmbedding`
- :class:`sklearn.manifold.TSNE`
- :func:`sklearn.manifold.trustworthiness`
- :class:`sklearn.semi_supervised.LabelPropagation`
- :class:`sklearn.semi_supervised.LabelSpreading`
For instance :class:`sklearn.neighbors.NearestNeighbors.kneighbors` and :class:`sklearn.neighbors.NearestNeighbors.radius_neighbors` can respectively be up to ×20 and ×5 faster than previously.
:pr:`21987`, :pr:`22064`, :pr:`22065`, :pr:`22288` and :pr:`22320` by :user:`Julien Jerphanion <jjerphan>`.
|Enhancement| All scikit-learn models now generate a more informative error message when some input contains unexpected NaN or infinite values. In particular the message contains the input name ("X", "y" or "sample_weight") and if an unexpected NaN value is found in X, the error message suggests potential solutions. :pr:`21219` by :user:`Olivier Grisel <ogrisel>`.
|Enhancement| All scikit-learn models now generate a more informative error message when setting invalid hyper-parameters with set_params. :pr:`21542` by :user:`Olivier Grisel <ogrisel>`.
|Enhancement| Removes random unique identifiers in the HTML representation. With this change, jupyter notebooks are reproducible as long as the cells are run in the same order. :pr:`23098` by `Thomas Fan`_.
|Fix| Estimators with non_deterministic tag set to True will skip both check_methods_sample_order_invariance and check_methods_subset_invariance tests. :pr:`22318` by :user:`Zhehao Liu <MaxwellLZH>`.
|API| The option for using the log loss, aka binomial or multinomial deviance, via the loss parameters was made more consistent. The preferred way is by setting the value to "log_loss". Old option names are still valid and produce the same models, but are deprecated and will be removed in version 1.3.
- For :class:`ensemble.GradientBoostingClassifier`, the loss parameter name "deviance" is deprecated in favor of the new name "log_loss", which is now the default. :pr:`23036` by :user:`Christian Lorentzen <lorentzenchr>`.
- For :class:`ensemble.HistGradientBoostingClassifier`, the loss parameter names "auto", "binary_crossentropy" and "categorical_crossentropy" are deprecated in favor of the new name "log_loss", which is now the default. :pr:`23040` by :user:`Christian Lorentzen <lorentzenchr>`.
- For :class:`linear_model.SGDClassifier`, the loss parameter name "log" is deprecated in favor of the new name "log_loss". :pr:`23046` by :user:`Christian Lorentzen <lorentzenchr>`.
|API| Rich html representation of estimators is now enabled by default in Jupyter notebooks. It can be deactivated by setting display='text' in :func:`sklearn.set_config`. :pr:`22856` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Enhancement| :func:`calibration.calibration_curve` accepts a parameter pos_label to specify the positive class label. :pr:`21032` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| :meth:`calibration.CalibratedClassifierCV.fit` now supports passing fit_params, which are routed to the base_estimator. :pr:`18170` by :user:`Benjamin Bossan <BenjaminBossan>`.
- |Enhancement| :class:`calibration.CalibrationDisplay` accepts a parameter pos_label to add this information to the plot. :pr:`21038` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :func:`calibration.calibration_curve` handles bin edges more consistently now. :pr:`14975` by `Andreas Müller`_ and :pr:`22526` by :user:`Meekail Zain <micky774>`.
- |API| :func:`calibration.calibration_curve`'s normalize parameter is now deprecated and will be removed in version 1.3. It is recommended that a proper probability (i.e. a classifier's :term:`predict_proba` positive class) is used for y_prob. :pr:`23095` by :user:`Jordan Silke <jsilke>`.
- |MajorFeature| :class:`BisectingKMeans` introducing Bisecting K-Means algorithm :pr:`20031` by :user:`Michal Krawczyk <michalkrawczyk>`, :user:`Tom Dupre la Tour <TomDLT>` and :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Enhancement| :class:`cluster.SpectralClustering` and :func:`cluster.spectral_clustering` now include the new 'cluster_qr' method that clusters samples in the embedding space as an alternative to the existing 'kmeans' and 'discrete' methods. See :func:`cluster.spectral_clustering` for more details. :pr:`21148` by :user:`Andrew Knyazev <lobpcg>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`cluster.Birch`, :class:`cluster.FeatureAgglomeration`, :class:`cluster.KMeans`, :class:`cluster.MiniBatchKMeans`. :pr:`22255` by `Thomas Fan`_.
- |Enhancement| :class:`cluster.SpectralClustering` now raises consistent error messages when passed invalid values for n_clusters, n_init, gamma, n_neighbors, eigen_tol or degree. :pr:`21881` by :user:`Hugo Vassard <hvassard>`.
- |Enhancement| :class:`cluster.AffinityPropagation` now returns cluster centers and labels if they exist, even if the model has not fully converged. When returning these potentially-degenerate cluster centers and labels, a new warning message is shown. If no cluster centers were constructed, then the cluster centers remain an empty list with labels set to -1 and the original warning message is shown. :pr:`22217` by :user:`Meekail Zain <micky774>`.
- |Efficiency| In :class:`cluster.KMeans`, the default
algorithmis now"lloyd"which is the full classical EM-style algorithm. Both"auto"and"full"are deprecated and will be removed in version 1.3. They are now aliases for"lloyd". The previous default was"auto", which relied on Elkan's algorithm. Lloyd's algorithm uses less memory than Elkan's, it is faster on many datasets, and its results are identical, hence the change. :pr:`21735` by :user:`Aurélien Geron <ageron>`. - |Fix| :class:`cluster.KMeans`'s init parameter now properly supports array-like input and NumPy string scalars. :pr:`22154` by `Thomas Fan`_.
- |Fix| :class:`compose.ColumnTransformer` now removes validation errors from __init__ and set_params methods. :pr:`22537` by :user:`iofall <iofall>` and :user:`Arisa Y. <arisayosh>`.
- |Fix| :term:`get_feature_names_out` functionality in :class:`compose.ColumnTransformer` was broken when columns were specified using slice. This is fixed in :pr:`22775` and :pr:`22913` by :user:`randomgeek78 <randomgeek78>`.
- |Fix| :class:`covariance.GraphicalLassoCV` now accepts NumPy array for the parameter alphas. :pr:`22493` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| the inverse_transform method of :class:`cross_decomposition.PLSRegression`, :class:`cross_decomposition.PLSCanonical` and :class:`cross_decomposition.CCA` now allows reconstruction of a X target when a Y parameter is given. :pr:`19680` by :user:`Robin Thibaut <robinthibaut>`.
- |Enhancement| Adds :term:`get_feature_names_out` to all transformers in the :mod:`~sklearn.cross_decomposition` module: :class:`cross_decomposition.CCA`, :class:`cross_decomposition.PLSSVD`, :class:`cross_decomposition.PLSRegression`, and :class:`cross_decomposition.PLSCanonical`. :pr:`22119` by `Thomas Fan`_.
- |Fix| The shape of the :term:`coef_` attribute of :class:`cross_decomposition.CCA`, :class:`cross_decomposition.PLSCanonical` and :class:`cross_decomposition.PLSRegression` will change in version 1.3, from (n_features, n_targets) to (n_targets, n_features), to be consistent with other linear models and to make it work with interface expecting a specific shape for coef_ (e.g. :class:`feature_selection.RFE`). :pr:`22016` by :user:`Guillaume Lemaitre <glemaitre>`.
- |API| add the fitted attribute intercept_ to :class:`cross_decomposition.PLSCanonical`, :class:`cross_decomposition.PLSRegression`, and :class:`cross_decomposition.CCA`. The method predict is indeed equivalent to Y = X @ coef_ + intercept_. :pr:`22015` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Feature| :func:`datasets.load_files` now accepts a ignore list and an allow list based on file extensions. :pr:`19747` by :user:`Tony Attalla <tonyattalla>` and :pr:`22498` by :user:`Meekail Zain <micky774>`.
- |Enhancement| :func:`datasets.make_swiss_roll` now supports the optional argument hole; when set to True, it returns the swiss-hole dataset. :pr:`21482` by :user:`Sebastian Pujalte <pujaltes>`.
- |Enhancement| :func:`datasets.make_blobs` no longer copies data during the generation process, therefore uses less memory. :pr:`22412` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Enhancement| :func:`datasets.load_diabetes` now accepts the parameter
scaled, to allow loading unscaled data. The scaled version of this dataset is now computed from the unscaled data, and can produce slightly different results that in previous version (within a 1e-4 absolute tolerance). :pr:`16605` by :user:`Mandy Gu <happilyeverafter95>`. - |Enhancement| :func:`datasets.fetch_openml` now has two optional arguments n_retries and delay. By default, :func:`datasets.fetch_openml` will retry 3 times in case of a network failure with a delay between each try. :pr:`21901` by :user:`Rileran <rileran>`.
- |Fix| :func:`datasets.fetch_covtype` is now concurrent-safe: data is downloaded to a temporary directory before being moved to the data directory. :pr:`23113` by :user:`Ilion Beyst <iasoon>`.
- |API| :func:`datasets.make_sparse_coded_signal` now accepts a parameter data_transposed to explicitly specify the shape of matrix X. The default behavior True is to return a transposed matrix X corresponding to a (n_features, n_samples) shape. The default value will change to False in version 1.3. :pr:`21425` by :user:`Gabriel Stefanini Vicente <g4brielvs>`.
|MajorFeature| Added a new estimator :class:`decomposition.MiniBatchNMF`. It is a faster but less accurate version of non-negative matrix factorization, better suited for large datasets. :pr:`16948` by :user:`Chiara Marmo <cmarmo>`, :user:`Patricio Cerda <pcerda>` and :user:`Jérémie du Boisberranger <jeremiedbb>`.
|Enhancement| :func:`decomposition.dict_learning`, :func:`decomposition.dict_learning_online` and :func:`decomposition.sparse_encode` preserve dtype for numpy.float32. :class:`decomposition.DictionaryLearning`, :class:`decomposition.MiniBatchDictionaryLearning` and :class:`decomposition.SparseCoder` preserve dtype for numpy.float32. :pr:`22002` by :user:`Takeshi Oura <takoika>`.
|Enhancement| :class:`decomposition.PCA` exposes a parameter n_oversamples to tune :func:`utils.randomized_svd` and get accurate results when the number of features is large. :pr:`21109` by :user:`Smile <x-shadow-man>`.
|Enhancement| The :class:`decomposition.MiniBatchDictionaryLearning` and :func:`decomposition.dict_learning_online` have been refactored and now have a stopping criterion based on a small change of the dictionary or objective function, controlled by the new max_iter, tol and max_no_improvement parameters. In addition, some of their parameters and attributes are deprecated.
- the n_iter parameter of both is deprecated. Use max_iter instead.
- the iter_offset, return_inner_stats, inner_stats and return_n_iter parameters of :func:`decomposition.dict_learning_online` serve internal purpose and are deprecated.
- the inner_stats_, iter_offset_ and random_state_ attributes of :class:`decomposition.MiniBatchDictionaryLearning` serve internal purpose and are deprecated.
- the default value of the batch_size parameter of both will change from 3 to 256 in version 1.3.
:pr:`18975` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
|Enhancement| :class:`decomposition.SparsePCA` and :class:`decomposition.MiniBatchSparsePCA` preserve dtype for numpy.float32. :pr:`22111` by :user:`Takeshi Oura <takoika>`.
|Enhancement| :class:`decomposition.TruncatedSVD` now allows n_components == n_features, if algorithm='randomized'. :pr:`22181` by :user:`Zach Deane-Mayer <zachmayer>`.
|Enhancement| Adds :term:`get_feature_names_out` to all transformers in the :mod:`~sklearn.decomposition` module: :class:`decomposition.DictionaryLearning`, :class:`decomposition.FactorAnalysis`, :class:`decomposition.FastICA`, :class:`decomposition.IncrementalPCA`, :class:`decomposition.KernelPCA`, :class:`decomposition.LatentDirichletAllocation`, :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.MiniBatchSparsePCA`, :class:`decomposition.NMF`, :class:`decomposition.PCA`, :class:`decomposition.SparsePCA`, and :class:`decomposition.TruncatedSVD`. :pr:`21334` by `Thomas Fan`_.
|Enhancement| :class:`decomposition.TruncatedSVD` exposes the parameter n_oversamples and power_iteration_normalizer to tune :func:`utils.randomized_svd` and get accurate results when the number of features is large, the rank of the matrix is high, or other features of the matrix make low rank approximation difficult. :pr:`21705` by :user:`Jay S. Stanley III <stanleyjs>`.
|Enhancement| :class:`decomposition.PCA` exposes the parameter power_iteration_normalizer to tune :func:`utils.randomized_svd` and get more accurate results when low rank approximation is difficult. :pr:`21705` by :user:`Jay S. Stanley III <stanleyjs>`.
|Fix| :class:`decomposition.FastICA` now validates input parameters in fit instead of __init__. :pr:`21432` by :user:`Hannah Bohle <hhnnhh>` and :user:`Maren Westermann <marenwestermann>`.
|Fix| :class:`decomposition.FastICA` now accepts np.float32 data without silent upcasting. The dtype is preserved by fit and fit_transform and the main fitted attributes use a dtype of the same precision as the training data. :pr:`22806` by :user:`Jihane Bennis <JihaneBennis>` and :user:`Olivier Grisel <ogrisel>`.
|Fix| :class:`decomposition.FactorAnalysis` now validates input parameters in fit instead of __init__. :pr:`21713` by :user:`Haya <HayaAlmutairi>` and :user:`Krum Arnaudov <krumeto>`.
|Fix| :class:`decomposition.KernelPCA` now validates input parameters in fit instead of __init__. :pr:`21567` by :user:`Maggie Chege <MaggieChege>`.
|Fix| :class:`decomposition.PCA` and :class:`decomposition.IncrementalPCA` more safely calculate precision using the inverse of the covariance matrix if self.noise_variance_ is zero. :pr:`22300` by :user:`Meekail Zain <micky774>` and :pr:`15948` by :user:`sysuresh`.
|Fix| Greatly reduced peak memory usage in :class:`decomposition.PCA` when calling fit or fit_transform. :pr:`22553` by :user:`Meekail Zain <micky774>`.
|API| :func:`decomposition.FastICA` now supports unit variance for whitening. The default value of its whiten argument will change from True (which behaves like 'arbitrary-variance') to 'unit-variance' in version 1.3. :pr:`19490` by :user:`Facundo Ferrin <fferrin>` and :user:`Julien Jerphanion <jjerphan>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`discriminant_analysis.LinearDiscriminantAnalysis`. :pr:`22120` by `Thomas Fan`_.
- |Fix| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now uses the correct variance-scaling coefficient which may result in different model behavior. :pr:`15984` by :user:`Okon Samuel <OkonSamuel>` and :pr:`22696` by :user:`Meekail Zain <micky774>`.
- |Fix| :class:`dummy.DummyRegressor` no longer overrides the constant parameter during fit. :pr:`22486` by `Thomas Fan`_.
- |MajorFeature| Added additional option loss="quantile" to :class:`ensemble.HistGradientBoostingRegressor` for modelling quantiles. The quantile level can be specified with the new parameter quantile. :pr:`21800` and :pr:`20567` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Efficiency| :meth:`fit` of :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` now calls :func:`utils.check_array` with parameter force_all_finite=False for non initial warm-start runs as it has already been checked before. :pr:`22159` by :user:`Geoffrey Paris <Geoffrey-Paris>`.
- |Enhancement| :class:`ensemble.HistGradientBoostingClassifier` is faster, for binary and in particular for multiclass problems thanks to the new private loss function module. :pr:`20811`, :pr:`20567` and :pr:`21814` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Enhancement| Adds support to use pre-fit models with cv="prefit" in :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`. :pr:`16748` by :user:`Siqi He <siqi-he>` and :pr:`22215` by :user:`Meekail Zain <micky774>`.
- |Enhancement| :class:`ensemble.RandomForestClassifier` and :class:`ensemble.ExtraTreesClassifier` have the new criterion="log_loss", which is equivalent to criterion="entropy". :pr:`23047` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`ensemble.VotingClassifier`, :class:`ensemble.VotingRegressor`, :class:`ensemble.StackingClassifier`, and :class:`ensemble.StackingRegressor`. :pr:`22695` and :pr:`22697` by `Thomas Fan`_.
- |Enhancement| :class:`ensemble.RandomTreesEmbedding` now has an informative :term:`get_feature_names_out` function that includes both tree index and leaf index in the output feature names. :pr:`21762` by :user:`Zhehao Liu <MaxwellLZH>` and `Thomas Fan`_.
- |Efficiency| Fitting a :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, and :class:`ensemble.RandomTreesEmbedding` is now faster in a multiprocessing setting, especially for subsequent fits with warm_start enabled. :pr:`22106` by :user:`Pieter Gijsbers <PGijsbers>`.
- |Efficiency| Improve runtime performance of :class:`ensemble.IsolationForest` by skipping repetitive input checks. :pr:`23149` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Fix| Change the parameter validation_fraction in :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` so that an error is raised if anything other than a float is passed in as an argument. :pr:`21632` by :user:`Genesis Valencia <genvalen>`.
- |Fix| Removed a potential source of CPU oversubscription in :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` when CPU resource usage is limited, for instance using cgroups quota in a docker container. :pr:`22566` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` no longer warns when fitting on a pandas DataFrame with a non-default scoring parameter and early_stopping enabled. :pr:`22908` by `Thomas Fan`_.
- |Fix| Fixes HTML repr for :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`. :pr:`23097` by `Thomas Fan`_.
- |API| The attribute loss_ of :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` has been deprecated and will be removed in version 1.3. :pr:`23079` by :user:`Christian Lorentzen <lorentzenchr>`.
- |API| Changed the default of max_features to 1.0 for :class:`ensemble.RandomForestRegressor` and to "sqrt" for :class:`ensemble.RandomForestClassifier`. Note that these give the same fit results as before, but are much easier to understand. The old default value "auto" has been deprecated and will be removed in version 1.3. The same changes are also applied for :class:`ensemble.ExtraTreesRegressor` and :class:`ensemble.ExtraTreesClassifier`. :pr:`20803` by :user:`Brian Sun <bsun94>`.
- |Feature| :class:`feature_extraction.FeatureHasher` now supports PyPy. :pr:`23023` by `Thomas Fan`_.
- |Fix| :class:`feature_extraction.FeatureHasher` now validates input parameters in transform instead of __init__. :pr:`21573` by :user:`Hannah Bohle <hhnnhh>` and :user:`Maren Westermann <marenwestermann>`.
- |Fix| :class:`feature_extraction.text.TfidfVectorizer` now does not create a :class:`feature_extraction.text.TfidfTransformer` at __init__ as required by our API. :pr:`21832` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Feature| Added auto mode to :class:`feature_selection.SequentialFeatureSelector`. If the argument n_features_to_select is 'auto', select features until the score improvement does not exceed the argument tol. The default value of n_features_to_select changed from None to 'warn' in 1.1 and will become 'auto' in 1.3. None and 'warn' will be removed in 1.3. :pr:`20145` by :user:`murata-yu <murata-yu>`.
- |Feature| Added the ability to pass callables to the max_features parameter of :class:`feature_selection.SelectFromModel`. Also introduced new attribute max_features_ which is inferred from max_features and the data during fit. If max_features is an integer, then max_features_ = max_features. If max_features is a callable, then max_features_ = max_features(X). :pr:`22356` by :user:`Meekail Zain <micky774>`.
- |Enhancement| :class:`feature_selection.GenericUnivariateSelect` preserves float32 dtype. :pr:`18482` by :user:`Thierry Gameiro <titigmr>` and :user:`Daniel Kharsa <aflatoune>` and :pr:`22370` by :user:`Meekail Zain <micky774>`.
- |Enhancement| Add a parameter force_finite to :func:`feature_selection.f_regression` and :func:`feature_selection.r_regression`. This parameter allows to force the output to be finite in the case where a feature or a the target is constant or that the feature and target are perfectly correlated (only for the F-statistic). :pr:`17819` by :user:`Juan Carlos Alfaro Jiménez <alfaro96>`.
- |Efficiency| Improve runtime performance of :func:`feature_selection.chi2` with boolean arrays. :pr:`22235` by `Thomas Fan`_.
- |Efficiency| Reduced memory usage of :func:`feature_selection.chi2`. :pr:`21837` by :user:`Louis Wagner <lrwagner>`.
- |Fix| predict and sample_y methods of :class:`gaussian_process.GaussianProcessRegressor` now return arrays of the correct shape in single-target and multi-target cases, and for both normalize_y=False and normalize_y=True. :pr:`22199` by :user:`Guillaume Lemaitre <glemaitre>`, :user:`Aidar Shakerimoff <AidarShakerimoff>` and :user:`Tenavi Nakamura-Zimmerer <Tenavi>`.
- |Fix| :class:`gaussian_process.GaussianProcessClassifier` raises a more informative error if CompoundKernel is passed via kernel. :pr:`22223` by :user:`MarcoM <marcozzxx810>`.
- |Enhancement| :class:`impute.SimpleImputer` now warns with feature names when features which are skipped due to the lack of any observed values in the training set. :pr:`21617` by :user:`Christian Ritter <chritter>`.
- |Enhancement| Added support for pd.NA in :class:`impute.SimpleImputer`. :pr:`21114` by :user:`Ying Xiong <yxiong>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`impute.SimpleImputer`, :class:`impute.KNNImputer`, :class:`impute.IterativeImputer`, and :class:`impute.MissingIndicator`. :pr:`21078` by `Thomas Fan`_.
- |API| The verbose parameter was deprecated for :class:`impute.SimpleImputer`. A warning will always be raised upon the removal of empty columns. :pr:`21448` by :user:`Oleh Kozynets <OlehKSS>` and :user:`Christian Ritter <chritter>`.
- |Feature| Add a display to plot the boundary decision of a classifier by using the method :func:`inspection.DecisionBoundaryDisplay.from_estimator`. :pr:`16061` by `Thomas Fan`_.
- |Enhancement| In :meth:`inspection.PartialDependenceDisplay.from_estimator`, allow kind to accept a list of strings to specify which type of plot to draw for each feature interaction. :pr:`19438` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| :meth:`inspection.PartialDependenceDisplay.from_estimator`, :meth:`inspection.PartialDependenceDisplay.plot`, and :func:`inspection.plot_partial_dependence` now support plotting centered Individual Conditional Expectation (cICE) and centered PDP curves controlled by setting the parameter centered. :pr:`18310` by :user:`Johannes Elfner <JoElfner>` and :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`isotonic.IsotonicRegression`. :pr:`22249` by `Thomas Fan`_.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`kernel_approximation.AdditiveChi2Sampler`. :class:`kernel_approximation.Nystroem`, :class:`kernel_approximation.PolynomialCountSketch`, :class:`kernel_approximation.RBFSampler`, and :class:`kernel_approximation.SkewedChi2Sampler`. :pr:`22137` and :pr:`22694` by `Thomas Fan`_.
- |Feature| :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, :class:`linear_model.Lasso` and :class:`linear_model.LassoCV` support sample_weight for sparse input X. :pr:`22808` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Feature| :class:`linear_model.Ridge` with solver="lsqr" now supports to fit sparse input with fit_intercept=True. :pr:`22950` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Enhancement| :class:`linear_model.QuantileRegressor` support sparse input for the highs based solvers. :pr:`21086` by :user:`Venkatachalam Natchiappan <venkyyuvy>`. In addition, those solvers now use the CSC matrix right from the beginning which speeds up fitting. :pr:`22206` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Enhancement| :class:`linear_model.LogisticRegression` is faster for
solvers="lbfgs"andsolver="newton-cg", for binary and in particular for multiclass problems thanks to the new private loss function module. In the multiclass case, the memory consumption has also been reduced for these solvers as the target is now label encoded (mapped to integers) instead of label binarized (one-hot encoded). The more classes, the larger the benefit. :pr:`21808`, :pr:`20567` and :pr:`21814` by :user:`Christian Lorentzen <lorentzenchr>`. - |Enhancement| :class:`linear_model.GammaRegressor`,
:class:`linear_model.PoissonRegressor` and :class:`linear_model.TweedieRegressor`
are faster for
solvers="lbfgs". :pr:`22548`, :pr:`21808` and :pr:`20567` by :user:`Christian Lorentzen <lorentzenchr>`. - |Enhancement| Rename parameter base_estimator to estimator in :class:`linear_model.RANSACRegressor` to improve readability and consistency. base_estimator is deprecated and will be removed in 1.3. :pr:`22062` by :user:`Adrian Trujillo <trujillo9616>`.
- |Enhancement| :func:`linear_model.ElasticNet` and and other linear model classes using coordinate descent show error messages when non-finite parameter weights are produced. :pr:`22148` by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
- |Enhancement| :class:`linear_model.ElasticNet` and :class:`linear_model.Lasso` now raise consistent error messages when passed invalid values for l1_ratio, alpha, max_iter and tol. :pr:`22240` by :user:`Arturo Amor <ArturoAmorQ>`.
- |Enhancement| :class:`linear_model.BayesianRidge` and :class:`linear_model.ARDRegression` now preserve float32 dtype. :pr:`9087` by :user:`Arthur Imbert <Henley13>` and :pr:`22525` by :user:`Meekail Zain <micky774>`.
- |Enhancement| :class:`linear_model.RidgeClassifier` is now supporting multilabel classification. :pr:`19689` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Enhancement| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV` now raise consistent error message when passed invalid values for alphas. :pr:`21606` by :user:`Arturo Amor <ArturoAmorQ>`.
- |Enhancement| :class:`linear_model.Ridge` and :class:`linear_model.RidgeClassifier` now raise consistent error message when passed invalid values for alpha, max_iter and tol. :pr:`21341` by :user:`Arturo Amor <ArturoAmorQ>`.
- |Enhancement| :func:`linear_model.orthogonal_mp_gram` preservse dtype for numpy.float32. :pr:`22002` by :user:`Takeshi Oura <takoika>`.
- |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| :class:`linear_model.TheilSenRegressor` now validates input parameter
max_subpopulationin fit instead of __init__. :pr:`21767` by :user:`Maren Westermann <marenwestermann>`. - |Fix| :class:`linear_model.ElasticNetCV` now produces correct warning when l1_ratio=0. :pr:`21724` by :user:`Yar Khine Phyo <yarkhinephyo>`.
- |Fix| :class:`linear_model.LogisticRegression` and :class:`linear_model.LogisticRegressionCV` now set the n_iter_ attribute with a shape that respects the docstring and that is consistent with the shape obtained when using the other solvers in the one-vs-rest setting. Previously, it would record only the maximum of the number of iterations for each binary sub-problem while now all of them are recorded. :pr:`21998` by :user:`Olivier Grisel <ogrisel>`.
- |Fix| The property family of :class:`linear_model.TweedieRegressor` is not validated in __init__ anymore. Instead, this (private) property is deprecated in :class:`linear_model.GammaRegressor`, :class:`linear_model.PoissonRegressor` and :class:`linear_model.TweedieRegressor`, and will be removed in 1.3. :pr:`22548` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Fix| The coef_ and intercept_ attributes of :class:`linear_model.LinearRegression` are now correctly computed in the presence of sample weights when the input is sparse. :pr:`22891` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| The coef_ and intercept_ attributes of :class:`linear_model.Ridge` with solver="sparse_cg" and solver="lbfgs" are now correctly computed in the presence of sample weights when the input is sparse. :pr:`22899` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| :class:`linear_model.SGDRegressor` and :class:`linear_model.SGDClassifier` now computes the validation error correctly when early stopping is enabled. :pr:`23256` by :user:`Zhehao Liu <MaxwellLZH>`.
- |API| :class:`linear_model.LassoLarsIC` now exposes noise_variance as a parameter in order to provide an estimate of the noise variance. This is particularly relevant when n_features > n_samples and the estimator of the noise variance cannot be computed. :pr:`21481` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Feature| :class:`manifold.Isomap` now supports radius-based neighbors via the radius argument. :pr:`19794` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Enhancement| :func:`manifold.spectral_embedding` and :class:`manifold.SpectralEmbedding` supports np.float32 dtype and will preserve this dtype. :pr:`21534` by :user:`Andrew Knyazev <lobpcg>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`manifold.Isomap` and :class:`manifold.LocallyLinearEmbedding`. :pr:`22254` by `Thomas Fan`_.
- |Enhancement| added metric_params to :class:`manifold.TSNE` constructor for additional parameters of distance metric to use in optimization. :pr:`21805` by :user:`Jeanne Dionisi <jeannedionisi>` and :pr:`22685` by :user:`Meekail Zain <micky774>`.
- |Enhancement| :func:`manifold.trustworthiness` raises an error if n_neighbours >= n_samples / 2 to ensure a correct support for the function. :pr:`18832` by :user:`Hong Shao Yang <hongshaoyang>` and :pr:`23033` by :user:`Meekail Zain <micky774>`.
- |Fix| :func:`manifold.spectral_embedding` now uses Gaussian instead of the previous uniform on [0, 1] random initial approximations to eigenvectors in eigen_solvers lobpcg and amg to improve their numerical stability. :pr:`21565` by :user:`Andrew Knyazev <lobpcg>`.
- |Feature| :func:`metrics.r2_score` and :func:`metrics.explained_variance_score` have a new force_finite parameter. Setting this parameter to False will return the actual non-finite score in case of perfect predictions or constant y_true, instead of the finite approximation (1.0 and 0.0 respectively) currently returned by default. :pr:`17266` by :user:`Sylvain Marié <smarie>`.
- |Feature| :func:`metrics.d2_pinball_score` and :func:`metrics.d2_absolute_error_score` calculate the D^2 regression score for the pinball loss and the absolute error respectively. :func:`metrics.d2_absolute_error_score` is a special case of :func:`metrics.d2_pinball_score` with a fixed quantile parameter alpha=0.5 for ease of use and discovery. The D^2 scores are generalizations of the r2_score and can be interpeted as the fraction of deviance explained. :pr:`22118` by :user:`Ohad Michel <ohadmich>`.
- |Enhancement| :func:`metrics.top_k_accuracy_score` raises an improved error message when y_true is binary and y_score is 2d. :pr:`22284` by `Thomas Fan`_.
- |Enhancement| :func:`metrics.roc_auc_score` now supports
average=Nonein the multiclass case whenmulticlass='ovr'which will return the score per class. :pr:`19158` by :user:`Nicki Skafte <SkafteNicki>`. - |Enhancement| Adds im_kw parameter to :meth:`metrics.ConfusionMatrixDisplay.from_estimator` :meth:`metrics.ConfusionMatrixDisplay.from_predictions`, and :meth:`metrics.ConfusionMatrixDisplay.plot`. The im_kw parameter is passed to the matplotlib.pyplot.imshow call when plotting the confusion matrix. :pr:`20753` by `Thomas Fan`_.
- |Fix| :func:`metrics.silhouette_score` now supports integer input for precomputed distances. :pr:`22108` by `Thomas Fan`_.
- |Fix| Fixed a bug in :func:`metrics.normalized_mutual_info_score` which could return unbounded values. :pr:`22635` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixes :func:`metrics.precision_recall_curve` and :func:`metrics.average_precision_score` when true labels are all negative. :pr:`19085` by :user:`Varun Agrawal <varunagrawal>`.
- |API| metrics.SCORERS is now deprecated and will be removed in 1.3. Please use :func:`metrics.get_scorer_names` to retrieve the names of all available scorers. :pr:`22866` by `Adrin Jalali`_.
- |API| Parameters
sample_weightandmultioutputof :func:`metrics.mean_absolute_percentage_error` are now keyword-only, in accordance with SLEP009. A deprecation cycle was introduced. :pr:`21576` by :user:`Paul-Emile Dugnat <pedugnat>`. - |API| The "wminkowski" metric of :class:`metrics.DistanceMetric` is deprecated and will be removed in version 1.3. Instead the existing "minkowski" metric now takes in an optional w parameter for weights. This deprecation aims at remaining consistent with SciPy 1.8 convention. :pr:`21873` by :user:`Yar Khine Phyo <yarkhinephyo>`.
- |API| :class:`metrics.DistanceMetric` has been moved from :mod:`sklearn.neighbors` to :mod:`sklearn.metrics`. Using neighbors.DistanceMetric for imports is still valid for backward compatibility, but this alias will be removed in 1.3. :pr:`21177` by :user:`Julien Jerphanion <jjerphan>`.
- |Enhancement| :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture` can now be initialized using k-means++ and random data points. :pr:`20408` by :user:`Gordon Walsh <g-walsh>`, :user:`Alberto Ceballos<alceballosa>` and :user:`Andres Rios<ariosramirez>`.
- |Fix| Fix a bug that correctly initialize precisions_cholesky_ in :class:`mixture.GaussianMixture` when providing precisions_init by taking its square root. :pr:`22058` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| :class:`mixture.GaussianMixture` now normalizes weights_ more safely, preventing rounding errors when calling :meth:`mixture.GaussianMixture.sample` with n_components=1. :pr:`23034` by :user:`Meekail Zain <micky774>`.
- |Enhancement| it is now possible to pass scoring="matthews_corrcoef" to all model selection tools with a scoring argument to use the Matthews correlation coefficient (MCC). :pr:`22203` by :user:`Olivier Grisel <ogrisel>`.
- |Enhancement| raise an error during cross-validation when the fits for all the splits failed. Similarly raise an error during grid-search when the fits for all the models and all the splits failed. :pr:`21026` by :user:`Loïc Estève <lesteve>`.
- |Fix| :class:`model_selection.GridSearchCV`, :class:`model_selection.HalvingGridSearchCV` now validate input parameters in fit instead of __init__. :pr:`21880` by :user:`Mrinal Tyagi <MrinalTyagi>`.
- |Fix| :func:`model_selection.learning_curve` now supports partial_fit with regressors. :pr:`22982` by `Thomas Fan`_.
- |Enhancement| :class:`multiclass.OneVsRestClassifier` now supports a verbose parameter so progress on fitting can be seen. :pr:`22508` by :user:`Chris Combs <combscCode>`.
- |Fix| :meth:`multiclass.OneVsOneClassifier.predict` returns correct predictions when the inner classifier only has a :term:`predict_proba`. :pr:`22604` by `Thomas Fan`_.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`neighbors.RadiusNeighborsTransformer`, :class:`neighbors.KNeighborsTransformer` and :class:`neighbors.NeighborhoodComponentsAnalysis`. :pr:`22212` by :user:`Meekail Zain <micky774>`.
- |Fix| :class:`neighbors.KernelDensity` now validates input parameters in fit instead of __init__. :pr:`21430` by :user:`Desislava Vasileva <DessyVV>` and :user:`Lucy Jimenez <LucyJimenez>`.
- |Fix| :func:`neighbors.KNeighborsRegressor.predict` now works properly when given an array-like input if KNeighborsRegressor is first constructed with a callable passed to the weights parameter. :pr:`22687` by :user:`Meekail Zain <micky774>`.
- |Enhancement| :func:`neural_network.MLPClassifier` and :func:`neural_network.MLPRegressor` show error messages when optimizers produce non-finite parameter weights. :pr:`22150` by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`neural_network.BernoulliRBM`. :pr:`22248` by `Thomas Fan`_.
- |Enhancement| Added support for "passthrough" in :class:`pipeline.FeatureUnion`. Setting a transformer to "passthrough" will pass the features unchanged. :pr:`20860` by :user:`Shubhraneel Pal <shubhraneel>`.
- |Fix| :class:`pipeline.Pipeline` now does not validate hyper-parameters in __init__ but in .fit(). :pr:`21888` by :user:`iofall <iofall>` and :user:`Arisa Y. <arisayosh>`.
- |Fix| :class:`pipeline.FeatureUnion` does not validate hyper-parameters in __init__. Validation is now handled in .fit() and .fit_transform(). :pr:`21954` by :user:`iofall <iofall>` and :user:`Arisa Y. <arisayosh>`.
- |Fix| Defines __sklearn_is_fitted__ in :class:`pipeline.FeatureUnion` to return correct result with :func:`utils.validation.check_is_fitted`. :pr:`22953` by :user:`randomgeek78 <randomgeek78>`.
- |Feature| :class:`preprocessing.OneHotEncoder` now supports grouping infrequent categories into a single feature. Grouping infrequent categories is enabled by specifying how to select infrequent categories with min_frequency or max_categories. :pr:`16018` by `Thomas Fan`_.
- |Enhancement| Adds a subsample parameter to :class:`preprocessing.KBinsDiscretizer`. This allows specifying a maximum number of samples to be used while fitting the model. The option is only available when strategy is set to quantile. :pr:`21445` by :user:`Felipe Bidu <fbidu>` and :user:`Amanda Dsouza <amy12xx>`.
- |Enhancement| Adds encoded_missing_value to :class:`preprocessing.OrdinalEncoder` to configure the encoded value for missing data. :pr:`21988` by `Thomas Fan`_.
- |Enhancement| Added the get_feature_names_out method and a new parameter feature_names_out to :class:`preprocessing.FunctionTransformer`. You can set feature_names_out to 'one-to-one' to use the input features names as the output feature names, or you can set it to a callable that returns the output feature names. This is especially useful when the transformer changes the number of features. If feature_names_out is None (which is the default), then get_output_feature_names is not defined. :pr:`21569` by :user:`Aurélien Geron <ageron>`.
- |Enhancement| Adds :term:`get_feature_names_out` to :class:`preprocessing.Normalizer`, :class:`preprocessing.KernelCenterer`, :class:`preprocessing.OrdinalEncoder`, and :class:`preprocessing.Binarizer`. :pr:`21079` by `Thomas Fan`_.
- |Fix| :class:`preprocessing.PowerTransformer` with method='yeo-johnson' better supports significantly non-Gaussian data when searching for an optimal lambda. :pr:`20653` by `Thomas Fan`_.
- |Fix| :class:`preprocessing.LabelBinarizer` now validates input parameters in fit instead of __init__. :pr:`21434` by :user:`Krum Arnaudov <krumeto>`.
- |Fix| :class:`preprocessing.FunctionTransformer` with check_inverse=True now provides informative error message when input has mixed dtypes. :pr:`19916` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Fix| :class:`preprocessing.KBinsDiscretizer` handles bin edges more consistently now. :pr:`14975` by `Andreas Müller`_ and :pr:`22526` by :user:`Meekail Zain <micky774>`.
- |Fix| Adds :meth:`preprocessing.KBinsDiscretizer.get_feature_names_out` support when encode="ordinal". :pr:`22735` by `Thomas Fan`_.
- |Enhancement| Adds an inverse_transform method and a compute_inverse_transform parameter to :class:`random_projection.GaussianRandomProjection` and :class:`random_projection.SparseRandomProjection`. When the parameter is set to True, the pseudo-inverse of the components is computed during fit and stored as inverse_components_. :pr:`21701` by :user:`Aurélien Geron <ageron>`.
- |Enhancement| :class:`random_projection.SparseRandomProjection` and :class:`random_projection.GaussianRandomProjection` preserves dtype for numpy.float32. :pr:`22114` by :user:`Takeshi Oura <takoika>`.
- |Enhancement| Adds :term:`get_feature_names_out` to all transformers in the :mod:`sklearn.random_projection` module: :class:`random_projection.GaussianRandomProjection` and :class:`random_projection.SparseRandomProjection`. :pr:`21330` by :user:`Loïc Estève <lesteve>`.
- |Enhancement| :class:`svm.OneClassSVM`, :class:`svm.NuSVC`, :class:`svm.NuSVR`, :class:`svm.SVC` and :class:`svm.SVR` now expose n_iter_, the number of iterations of the libsvm optimization routine. :pr:`21408` by :user:`Juan Martín Loyola <jmloyola>`.
- |Enhancement| :func:`svm.SVR`, :func:`svm.SVC`, :func:`svm.NuSVR`, :func:`svm.OneClassSVM`, :func:`svm.NuSVC` now raise an error when the dual-gap estimation produce non-finite parameter weights. :pr:`22149` by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
- |Fix| :class:`svm.NuSVC`, :class:`svm.NuSVR`, :class:`svm.SVC`, :class:`svm.SVR`, :class:`svm.OneClassSVM` now validate input parameters in fit instead of __init__. :pr:`21436` by :user:`Haidar Almubarak <Haidar13 >`.
- |Enhancement| :class:`tree.DecisionTreeClassifier` and :class:`tree.ExtraTreeClassifier` have the new criterion="log_loss", which is equivalent to criterion="entropy". :pr:`23047` by :user:`Christian Lorentzen <lorentzenchr>`.
- |Fix| Fix a bug in the Poisson splitting criterion for :class:`tree.DecisionTreeRegressor`. :pr:`22191` by :user:`Christian Lorentzen <lorentzenchr>`.
- |API| Changed the default value of max_features to 1.0 for :class:`tree.ExtraTreeRegressor` and to "sqrt" for :class:`tree.ExtraTreeClassifier`, which will not change the fit result. The original default value "auto" has been deprecated and will be removed in version 1.3. Setting max_features to "auto" is also deprecated for :class:`tree.DecisionTreeClassifier` and :class:`tree.DecisionTreeRegressor`. :pr:`22476` by :user:`Zhehao Liu <MaxwellLZH>`.
- |Enhancement| :func:`utils.check_array` and :func:`utils.multiclass.type_of_target` now accept an input_name parameter to make the error message more informative when passed invalid input data (e.g. with NaN or infinite values). :pr:`21219` by :user:`Olivier Grisel <ogrisel>`.
- |Enhancement| :func:`utils.check_array` returns a float ndarray with np.nan when passed a Float32 or Float64 pandas extension array with pd.NA. :pr:`21278` by `Thomas Fan`_.
- |Enhancement| :func:`utils.estimator_html_repr` shows a more helpful error message when running in a jupyter notebook that is not trusted. :pr:`21316` by `Thomas Fan`_.
- |Enhancement| :func:`utils.estimator_html_repr` displays an arrow on the top left corner of the HTML representation to show how the elements are clickable. :pr:`21298` by `Thomas Fan`_.
- |Enhancement| :func:`utils.check_array` with dtype=None returns numeric arrays when passed in a pandas DataFrame with mixed dtypes. dtype="numeric" will also make better infer the dtype when the DataFrame has mixed dtypes. :pr:`22237` by `Thomas Fan`_.
- |Enhancement| :func:`utils.check_scalar` now has better messages when displaying the type. :pr:`22218` by `Thomas Fan`_.
- |Fix| Changes the error message of the ValidationError raised by :func:`utils.check_X_y` when y is None so that it is compatible with the check_requires_y_none estimator check. :pr:`22578` by :user:`Claudio Salvatore Arcidiacono <ClaudioSalvatoreArcidiacono>`.
- |Fix| :func:`utils.class_weight.compute_class_weight` now only requires that all classes in y have a weight in class_weight. An error is still raised when a class is present in y but not in class_weight. :pr:`22595` by `Thomas Fan`_.
- |Fix| :func:`utils.estimator_html_repr` has an improved visualization for nested meta-estimators. :pr:`21310` by `Thomas Fan`_.
- |Fix| :func:`utils.check_scalar` raises an error when include_boundaries={"left", "right"} and the boundaries are not set. :pr:`22027` by :user:`Marie Lanternier <mlant>`.
- |Fix| :func:`utils.metaestimators.available_if` correctly returns a bounded method that can be pickled. :pr:`23077` by `Thomas Fan`_.
- |API| :func:`utils.estimator_checks.check_estimator`'s argument is now called estimator (previous name was Estimator). :pr:`22188` by :user:`Mathurin Massias <mathurinm>`.
- |API| :func:`utils.metaestimators.if_delegate_has_method` is deprecated and will be removed in version 1.3. Use :func:`utils.metaestimators.available_if` instead. :pr:`22830` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.0, including:
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