Version 1.4.0

In Development

Legend for changelogs

• Major Feature : something big that you couldn’t do before.

• Feature : something that you couldn’t do before.

• Efficiency : an existing feature now may not require as much computation or memory.

• Enhancement : a miscellaneous minor improvement.

• Fix : something that previously didn’t work as documentated – or according to reasonable expectations – should now work.

• API Change : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

Changed models

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

Changes impacting all modules

• Enhancement All estimators now recognizes the column names from any dataframe that adopts the DataFrame Interchange Protocol. Dataframes that return a correct representation through np.asarray(df) is expected to work with our estimators and functions. #26464 by Thomas Fan.

Metadata Routing

The following models now support metadata routing in one or more or their methods. Refer to the Metadata Routing User Guide for more details.

• Feature pipeline.Pipeline now supports metadata routing according to metadata routing user guide. #26789 by Adrin Jalali.

• Feature cross_validate, cross_val_score, and cross_val_predict now support metadata routing. The metadata are routed to the estimator’s fit, the scorer, and the CV splitter’s split. The metadata is accepted via the new params parameter. fit_params is deprecated and will be removed in version 1.6. groups parameter is also not accepted as a separate argument when metadata routing is enabled and should be passed via the params parameter. #26896 by Adrin Jalali.

• Feature GridSearchCV, RandomizedSearchCV, HalvingGridSearchCV, and HalvingRandomSearchCV now support metadata routing in their fit and score, and route metadata to the underlying estimator’s fit, the CV splitter, and the scorer. #27058 by Adrin Jalali.

• Enhancement ColumnTransformer now supports metadata routing according to metadata routing user guide. #27005 by Adrin Jalali.

• Enhancement linear_model.LogisticRegressionCV now supports metadata routing. linear_model.LogisticRegressionCV.fit now accepts **params which are passed to the underlying splitter and scorer. linear_model.LogisticRegressionCV.score now accepts **score_params which are passed to the underlying scorer. #26525 by Omar Salman.

Support for SciPy sparse arrays

Several estimators are now supporting SciPy sparse arrays. The following functions and classes are impacted:

Functions:

• decomposition.non_negative_factorization in #27100 by Isaac Virshup;

• metrics.f_regression in #27239 by Yaroslav Korobko;

• metrics.r_regression in #27239 by Yaroslav Korobko;

• sklearn.utils.multiclass.type_of_target in #27274 by Yao Xiao.

Classes:

• decomposition.NMF in #27100 by Isaac Virshup;

• decomposition.MiniBatchNMF in #27100 by Isaac Virshup;

• feature_extraction.text.TfidfTransformer in #27219 by Yao Xiao;

• impute.SimpleImputer in #27277 by Yao Xiao;

• impute.IterativeImputer in #27277 by Yao Xiao;

• impute.KNNImputer in #27277 by Yao Xiao;

• kernel_approximation.PolynomialCountSketch in #27301 by Lohit SundaramahaLingam;

• neural_network.BernoulliRBM in #27252 by Yao Xiao <Charlie-XIAO>.

Changelog

sklearn.base

• Enhancement base.ClusterMixin.fit_predict and base.OutlierMixin.fit_predict now accept **kwargs which are passed to the fit method of the estimator. #26506 by Adrin Jalali.

• Enhancement base.TransformerMixin.fit_transform and base.OutlierMixin.fit_predict now raise a warning if transform / predict consume metadata, but no custom fit_transform / fit_predict is defined in the class inheriting from them correspondingly. #26831 by Adrin Jalali.

• Enhancement base.clone now supports dict as input and creates a copy. #26786 by Adrin Jalali.

• API Change process_routing now has a different signature. The first two (the object and the method) are positional only, and all metadata are passed as keyword arguments. #26909 by Adrin Jalali.

sklearn.calibration

• Enhancement The internal objective and gradient of the sigmoid method of calibration.CalibratedClassifierCV have been replaced by the private loss module. #27185 by Omar Salman.

sklearn.cluster

• API Change : kdtree and balltree values are now deprecated and are renamed as kd_tree and ball_tree respectively for the algorithm parameter of cluster.HDBSCAN ensuring consistency in naming convention. kdtree and balltree values will be removed in 1.6. #26744 by Shreesha Kumar Bhat.

sklearn.decomposition

• Enhancement An “auto” option was added to the n_components parameter of decomposition.non_negative_factorization, decomposition.NMF and decomposition.MiniBatchNMF to automatically infer the number of components from W or H shapes when using a custom initialization. The default value of this parameter will change from None to auto in version 1.6. #26634 by Alexandre Landeau and Alexandre Vigny.

• Enhancement decomposition.PCA now supports the Array API for the full and randomized solvers (with QR power iterations). See Array API support (experimental) for more details. #26315 and #27098 by Mateusz Sokół, Olivier Grisel and Edoardo Abati.

sklearn.ensemble

• Major Feature ensemble.RandomForestClassifier and ensemble.RandomForestRegressor support missing values when the criterion is gini, entropy, or log_loss, for classification or squared_error, friedman_mse, or poisson for regression. #26391 by Thomas Fan.

• Feature ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier and ensemble.ExtraTreesRegressor now support monotonic constraints, useful when features are supposed to have a positive/negative effect on the target. Missing values in the train data and multi-output targets are not supported. #13649 by Samuel Ronsin, initiated by Patrick O’Reilly.

• Efficiency ensemble.GradientBoostingClassifier is faster, for binary and in particular for multiclass problems thanks to the private loss function module. #26278 by Christian Lorentzen.

• Efficiency Improves runtime and memory usage for ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor when trained on sparse data. #26957 by Thomas Fan.

• API Change In AdaBoostClassifier, the algorithm argument SAMME.R was deprecated and will be removed in 1.6. #26830 by Stefanie Senger.

sklearn.linear_model

• Enhancement Solver "newton-cg" in LogisticRegression and LogisticRegressionCV uses a little less memory. The effect is proportional to the number of coefficients (n_features * n_classes). #27417 by Christian Lorentzen.

sklearn.metrics

• Efficiency Computing pairwise distances via metrics.DistanceMetric for CSR × CSR, Dense × CSR, and CSR × Dense datasets is now 1.5x faster. #26765 by Meekail Zain

• Efficiency Computing distances via metrics.DistanceMetric for CSR × CSR, Dense × CSR, and CSR × Dense now uses ~50% less memory, and outputs distances in the same dtype as the provided data. #27006 by Meekail Zain

• Enhancement Improve the rendering of the plot obtained with the metrics.PrecisionRecallDisplay and metrics.RocCurveDisplay classes. the x- and y-axis limits are set to [0, 1] and the aspect ratio between both axis is set to be 1 to get a square plot. #26366 by Mojdeh Rastgoo.

• Enhancement Added neg_root_mean_squared_log_error_scorer as scorer #26734 by Alejandro Martin Gil.

• Enhancement sklearn.metrics.accuracy_score and sklearn.metrics.zero_one_loss now support Array API compatible inputs. #27137 by Edoardo Abati.

• API Change The squared parameter of metrics.mean_squared_error and metrics.mean_squared_log_error is deprecated and will be removed in 1.6. Use the new functions metrics.root_mean_squared_error and root_mean_squared_log_error instead. #26734 by Alejandro Martin Gil.

sklearn.model_selection

• Enhancement sklearn.model_selection.train_test_split now supports Array API compatible inputs. #26855 by Tim Head.

• Fix model_selection.GridSearchCV, model_selection.RandomizedSearchCV, and model_selection.HalvingGridSearchCV now don’t change the given object in the parameter grid if it’s an estimator. #26786 by Adrin Jalali.

sklearn.neighbors

• Efficiency sklearn.neighbors.KNeighborsRegressor.predict and sklearn.neighbors.KNeighborsRegressor.predict_proba now efficiently support pairs of dense and sparse datasets. #27018 by Julien Jerphanion.

• API Change neighbors.KNeighborsRegressor now accepts metric.DistanceMetric objects directly via the metric keyword argument allowing for the use of accelerated third-party metric.DistanceMetric objects. #26267 by Meekail Zain

sklearn.preprocessing

• Major Feature preprocessing.MinMaxScaler and preprocessing.MaxAbsScaler now support the Array API. Array API support is considered experimental and might evolve without being subject to our usual rolling deprecation cycle policy. See Array API support (experimental) for more details. #26243 by Tim Head and #27110 by Edoardo Abati.

• Efficiency preprocessing.OrdinalEncoder avoids calculating missing indices twice to improve efficiency. #27017 by Xuefeng Xu.

• Enhancement Improves warnings in preprocessing.FunctionTransfomer when func returns a pandas dataframe and the output is configured to be pandas. #26944 by Thomas Fan.

• Enhancement preprocessing.TargetEncoder now supports target_type ‘multiclass’. #26674 by Lucy Liu.

sklearn.tree

• Feature tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier and tree.ExtraTreeRegressor now support monotonic constraints, useful when features are supposed to have a positive/negative effect on the target. Missing values in the train data and multi-output targets are not supported. #13649 by Samuel Ronsin, initiated by Patrick O’Reilly.

sklearn.utils

• Enhancement sklearn.utils.estimator_html_repr dynamically adapts diagram colors based on the browser’s prefers-color-scheme, providing improved adaptability to dark mode environments. #26862 by Andrew Goh Yisheng, Thomas Fan, Adrin Jalali.

• Enhancement MetadataRequest and MetadataRouter now have a consumes method which can be used to check whether a given set of parameters would be consumed. #26831 by Adrin Jalali.

• Fix sklearn.utils.check_array should accept both matrix and array from the sparse SciPy module. The previous implementation would fail if copy=True by calling specific NumPy np.may_share_memory that does not work with SciPy sparse array and does not return the correct result for SciPy sparse matrix. #27336 by Guillaume Lemaitre.

Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.3, including:

TODO: update at the time of the release.