Version 1.7#

Changes impacting many modules#

• Sparse update: As part of the SciPy change from spmatrix to sparray, all internal use of sparse now supports both sparray and spmatrix. All manipulations of sparse objects should work for either spmatrix or sparray. This is pass 1 of a migration toward sparray (see SciPy migration to sparray By Dan Schult #30858

Support for Array API#

Additional estimators and functions have been updated to include support for all Array API compliant inputs.

See Array API support (experimental) for more details.

• Feature sklearn.utils.check_consistent_length now supports Array API compatible inputs. By Stefanie Senger #29519

• Feature sklearn.metrics.explained_variance_score and sklearn.metrics.mean_pinball_loss now support Array API compatible inputs. By Virgil Chan #29978

• Feature sklearn.metrics.fbeta_score, sklearn.metrics.precision_score and sklearn.metrics.recall_score now support Array API compatible inputs. By Omar Salman #30395

• Feature sklearn.metrics.hamming_loss now support Array API compatible inputs. By Thomas Li #30838

• array-api-compat and array-api-extra are now vendored within the scikit-learn source. Users of the experimental array API standard support no longer need to install array-api-compat in their environemnt. by Lucas Colley #30340

Metadata routing#

Refer to the Metadata Routing User Guide for more details.

• Feature ensemble.BaggingClassifier and ensemble.BaggingRegressor now support metadata routing through their predict, predict_proba, predict_log_proba and decision_function methods and pass **params to the underlying estimators. By Stefanie Senger. #30833

sklearn.calibration#

• Fix CalibratedClassifierCV now raises FutureWarning instead of UserWarning when passing cv="prefit”. By Olivier Grisel

• CalibratedClassifierCV with method="sigmoid" no longer crashes when passing float64-dtyped sample_weight along with a base estimator that outputs float32-dtyped predictions. By Olivier Grisel #30873

sklearn.covariance#

• Fix Support for n_samples == n_features in sklearn.covariance.MinCovDet has been restored. By Antony Lee. #30483

sklearn.datasets#

• Enhancement New parameter return_X_y added to datasets.make_classification. The default value of the parameter does not change how the function behaves. By Success Moses and Adam Cooper #30196

sklearn.decomposition#

• Feature DictionaryLearning, SparseCoder and MiniBatchDictionaryLearning now have a inverse_transform method. By Rémi Flamary #30443

sklearn.ensemble#

• Fix ensemble.VotingClassifier and ensemble.VotingRegressor validate estimators to make sure it is a list of tuples. By Thomas Fan. #30649

sklearn.inspection#

• Enhancement Add custom_values parameter in inspection.partial_dependence. It enables users to pass their own grid of values at which the partial dependence should be calculated. By Freddy A. Boulton and Stephen Pardy #26202

• Enhancement inspection.DecisionBoundaryDisplay now supports plotting all classes for multi-class problems when response_method is ‘decision_function’, ‘predict_proba’ or ‘auto’. By Lucy Liu #29797

• API Change inspection.partial_dependence does no longer accept integer dtype for numerical feature columns. Explicit conversion to floating point values is now required before calling this tool (and preferably even before fitting the model to inspect). By Olivier Grisel #30409

sklearn.linear_model#

• Fix linear_model.LogisticRegression and linear_model.LogisticRegressionCV now properly pass sample weights to utils.class_weight.compute_class_weight when fit with class_weight="balanced". By Shruti Nath and Olivier Grisel #30057

• Fix Enhancement Added a new parameter tol to

  linear_model.LinearRegression that determines the precision of the solution coef_ when fitting on sparse data. By Success Moses #30521

• Fix The update and initialization of the hyperparameters now properly handle

  sample weights in linear_model.BayesianRidge. By Antoine Baker. #30644

sklearn.metrics#

• Feature metrics.brier_score_loss implements the Brier score for multiclass classification problems and adds a scale_by_half argument. This metric is notably useful to assess both sharpness and calibration of probabilistic classifiers. See the docstrings for more details. By Varun Aggarwal, Olivier Grisel and Antoine Baker. #22046

• Enhancement class_likelihood_ratios now has a replace_undefined_by param. When there is a division by zero, the metric is undefined and the set values are returned for LR+ and LR-. By Stefanie Senger #29288

• Fix metrics.log_loss now raises a ValueError if values of y_true are missing in labels. By Varun Aggarwal, Olivier Grisel and Antoine Baker. #22046

• Fix class_likelihood_ratios now raises UndefinedMetricWarning instead of UserWarning when a division by zero occurs. By Stefanie Senger #29288

• Fix metrics.RocCurveDisplay will no longer set a legend when label is None in both the line_kwargs and the chance_level_kw. By Arturo Amor #29727

sklearn.mixture#

• Feature Added an attribute lower_bounds_ in the mixture.BaseMixture class to save the list of lower bounds for each iteration thereby providing insights into the convergence behavior of mixture models like mixture.GaussianMixture. By Manideep Yenugula #28559

• Efficiency Simplified redundant computation when estimating covariances in GaussianMixture with a covariance_type="spherical" or covariance_type="diag". By Leonce Mekinda and Olivier Grisel #30414

• Efficiency GaussianMixture now consistently operates at float32 precision when fitted with float32 data to improve training speed and memory efficiency. Previously, part of the computation would be implicitly cast to float64. By Olivier Grisel and Omar Salman. #30415

sklearn.model_selection#

• Fix Hyper-parameter optimizers such as model_selection.GridSearchCV now forward sample_weight to the scorer even when metadata routing is not enabled. By Antoine Baker #30743

sklearn.multioutput#

• Enhancement The parameter base_estimator has been deprecated in favour of estimator for multioutput.RegressorChain and multioutput.ClassifierChain. By Success Moses and dikraMasrour #30152

sklearn.neural_network#

• Feature Added support for sample_weight in neural_network.MLPClassifier and neural_network.MLPRegressor. By Zach Shu and Christian Lorentzen #30155

sklearn.pipeline#

• Enhancement Expose the verbose_feature_names_out argument in the pipeline.make_union function, allowing users to control feature name uniqueness in the pipeline.FeatureUnion. By Abhijeetsingh Meena #30406

sklearn.preprocessing#

• Enhancement preprocessing.KBinsDiscretizer with strategy="uniform" now accepts sample_weight. Additionally with strategy="quantile" the quantile_method can now be specified (in the future quantile_method="averaged_inverted_cdf" will become the default) #29907 by Shruti Nath and Olivier Grisel #29907

• Fix preprocessing.KBinsDiscretizer now uses weighted resampling when sample weights are given and subsampling is used. This may change results even when not using sample weights, although in absolute and not in terms of statistical properties. #29907 by Shruti Nath and Jérémie du Boisberranger #29907

sklearn.svm#

• Fix svm.LinearSVC now properly passes sample weights to utils.class_weight.compute_class_weight when fit with class_weight="balanced". By Shruti Nath #30057

sklearn.utils#

• Enhancement utils.class_weight.compute_class_weight now properly accounts for sample weights when using strategy “balanced” to calculate class weights. By Shruti Nath #30057

• Enhancement Warning filters from the main process are propagated to joblib workers. By Thomas Fan #30380

Enhancement - :func: resample now handles sample weights which allows

weighted resampling. #29907 by Shruti Nath and Olivier Grisel #29907

• Fix In utils.estimator_checks we now enforce for binary classifiers a

  binary y by taking the minimum as the negative class instead of the first element, which makes it robust to y shuffling. It prevents two checks from wrongly failing on binary classifiers. By Antoine Baker. #30775

Code and documentation contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.7, including:

TODO: update at the time of the release.