Version 1.8

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 documented – 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.

Version 1.8.dev0

September 2025

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.preprocessing.StandardScaler now supports Array API compliant inputs. #27113 by Alexander Fabisch, Edoardo Abati, Olivier Grisel and Charles Hill. #27113

• Feature sklearn.metrics.confusion_matrix now supports Array API compatible inputs. By Stefanie Senger #30562

• Feature sklearn.gaussian_mixture.GaussianMixture with init_params="random" or init_params="random_from_data" and warm_start=False now supports Array API compatible inputs. By Stefanie Senger and Loïc Estève #30777

• Feature sklearn.metrics.roc_curve now supports Array API compatible inputs. By Thomas Li #30878

• Feature preprocessing.PolynomialFeatures now supports array API compatible inputs. By Omar Salman #31580

• Enhancement metrics.pairwise.pairwise_kernels now supports Array API compatible inputs, when the underling metric does (the only metric NOT currently supported is sklearn.metrics.pairwise.laplacian_kernel). By Emily Chen and Lucy Liu.

• metrics.pairwise.pairwise_distances now supports Array API compatible inputs, when the underlying metric does (currently “cosine”, “euclidean” and “l2”). By Emily Chen and Lucy Liu. #29822

Metadata routing

Refer to the Metadata Routing User Guide for more details.

• Fix Fixed an issue where passing sample_weight to a Pipeline inside a GridSearchCV would raise an error with metadata routing enabled. By Adrin Jalali. #31898

sklearn.base

• Feature Refactored :method:`dir` in BaseEstimator to recognize condition check in :method:`available_if`. By John Hendricks and Miguel Parece. #31928

sklearn.calibration

• Feature Added temperature scaling method in caliabration.CalibratedClassifierCV. By Virgil Chan and Christian Lorentzen. #31068

sklearn.cluster

• Efficiency cluster.kmeans_plusplus now uses np.cumsum directly without extra numerical stability checks and without casting to np.float64. By Tiziano Zito #31991

sklearn.compose

• Fix compose.TransformedTargetRegressor now passes the transformed target to the regressor with the same number of dimensions as the original target. By kryggird. #31563

sklearn.decomposition

• Fix Add input checks to the inverse_transform method of decomposition.PCA and decomposition.IncrementalPCA. #29310 by Ian Faust. #29310

sklearn.ensemble

• Fix ensemble.BaggingClassifier, ensemble.BaggingRegressor and ensemble.IsolationForest now use sample_weight to draw the samples instead of forwarding them multiplied by a uniformly sampled mask to the underlying estimators. Furthermore, max_samples is now interpreted as a fraction of sample_weight.sum() instead of X.shape[0] when passed as a float. By Antoine Baker. #31414

sklearn.feature_extraction

• Fix Set the tag requires_fit=False for the classes feature_extraction.FeatureHasher and feature_extraction.HashingVectorizer. By hakan çanakcı. #31851

sklearn.gaussian_process

• Efficiency make GaussianProcessRegressor.predict faster when return_cov and return_std are both False. By Rafael Ayllón Gavilán. #31431

sklearn.impute

• Fix Fixed a bug in impute.SimpleImputer with strategy="most_frequent" when there is a tie in the most frequent value and the input data has mixed types. By Alexandre Abraham. #31820

sklearn.linear_model

• Efficiency class:linear_model:ElasticNet and class:linear_model:Lasso with precompute=False use less memory for dense X and are a bit faster. Previously, they used twice the memory of X even for Fortran-contiguous X. By Christian Lorentzen #31665

• Efficiency linear_model.ElasticNet and linear_model.Lasso avoid double input checking and are therefore a bit faster. By Christian Lorentzen. #31848

• Efficiency linear_model.ElasticNet, linear_model.ElasticNetCV, linear_model.Lasso, linear_model.LassoCV, linear_model.MultiTaskElasticNet, linear_model.MultiTaskElasticNetCV, linear_model.MultiTaskLasso and linear_model.MultiTaskLassoCV are faster to fit by avoiding a BLAS level 1 (axpy) call in the innermost loop. Same for functions linear_model.enet_path and linear_model.lasso_path. By Christian Lorentzen #31956 and #31880

• Efficiency linear_model.ElasticNet, linear_model.ElasticNetCV, linear_model.Lasso, linear_model.LassoCV as well as linear_model.lasso_path and linear_model.enet_path now implement gap safe screening rules in the coordinate descent solver for dense X and precompute=False or "auto" with n_samples < n_features. The speedup of fitting time is particularly pronounced (10-times is possible) when computing regularization paths like the *CV-variants of the above estimators do. There is now an additional check of the stopping criterion before entering the main loop of descent steps. As the stopping criterion requires the computation of the dual gap, the screening happens whenever the dual gap is computed. By Christian Lorentzen. #31882

• Efficiency linear_model.ElasticNetCV, linear_model.LassoCV, linear_model.MultiTaskElasticNetCV and linear_model.MultiTaskLassoCV avoid an additional copy of X with default copy_X=True. By Christian Lorentzen. #31946

• Enhancement linear_model.ElasticNet, linear_model.ElasticNetCV, linear_model.Lasso, linear_model.LassoCV, MultiTaskElasticNet, MultiTaskElasticNetCV, MultiTaskLasso, MultiTaskLassoCV, as well as linear_model.enet_path and linear_model.lasso_path now use dual gap <= tol instead of dual gap < tol as stopping criterion. The resulting coefficients might differ to previous versions of scikit-learn in rare cases. By Christian Lorentzen. #31906

• Fix Fixed a bug in class:linear_model:LogisticRegression when used with

solver="newton-cholesky"`and `warm_start=True on multi-class problems, either

with fit_intercept=True or with penalty=None (both resulting in unpenalized parameters for the solver). The coefficients and intercepts of the last class as provided by warm start were partially wrongly overwritten by zero. By Christian Lorentzen #31866

• API Change PassiveAggressiveClassifier and PassiveAggressiveRegressor are deprecated and will be removed in 1.10. Equivalent estimators are available with SGDClassifier and SGDRegressor, both of which expose the options learning_rate="pa1" and "pa2". The parameter eta0 can be used to specify the aggressiveness parameter of the Passive-Aggressive-Algorithms, called C in the reference paper. By Christian Lorentzen #31932 and #29097

• API Change linear_model.SGDClassifier, linear_model.SGDRegressor, and linear_model.SGDOneClassSVM now deprecate negative values for the power_t parameter. Using a negative value will raise a warning in version 1.8 and will raise an error in version 1.10. A value in the range [0.0, inf) must be used instead. By Ritvi Alagusankar #31474

sklearn.metrics

• Feature metrics.d2_brier_score has been added which calculates the D^2 for the Brier score. By Omar Salman. #28971

• Enhancement metrics.median_absolute_error now supports Array API compatible inputs. By Lucy Liu. #31406

• Fix metrics.median_absolute_error now uses _averaged_weighted_percentile instead of _weighted_percentile to calculate median when sample_weight is not None. This is equivalent to using the “averaged_inverted_cdf” instead of the “inverted_cdf” quantile method, which gives results equivalent to numpy.median if equal weights used. By Lucy Liu #30787

• Fix y_pred is deprecated in favour of y_score in metrics.DetCurveDisplay.from_predictions and metrics.PrecisionRecallDisplay.from_predictions. y_pred will be removed in v1.10. By Luis #31764

• Fix repr on a scorer which has been created with a partial score_func now correctly works and uses the repr of the given partial object. By Adrin Jalali. #31891

Fix - Additional sample_weight checking has been added to

metrics.accuracy_score, metrics.balanced_accuracy_score, metrics.brier_score_loss, metrics.class_likelihood_ratios, metrics.classification_report, metrics.cohen_kappa_score, metrics.confusion_matrix, metrics.f1_score, metrics.fbeta_score, metrics.hamming_loss, metrics.jaccard_score, metrics.matthews_corrcoef, metrics.multilabel_confusion_matrix, metrics.precision_recall_fscore_support, metrics.precision_score, metrics.recall_score and metrics.zero_one_loss. sample_weight can only be 1D, consistent to y_true and y_pred in length,and all values must be finite and not complex. By Lucy Liu. #31701

• API Change metrics.cluster.entropy is deprecated and will be removed in v1.10. By Lucy Liu #31294

sklearn.multiclass

• Fix Fix tie-breaking behavior in multiclass.OneVsRestClassifier to match np.argmax tie-breaking behavior. By Lakshmi Krishnan. #15504

sklearn.pipeline

• Fix pipeline.FeatureUnion now validates that all transformers return 2D outputs and raises an informative error when transformers return 1D outputs, preventing silent failures that previously produced meaningless concatenated results. By gguiomar. #31559

sklearn.preprocessing

• Enhancement preprocessing.SplineTransformer can now handle missing values with the parameter handle_missing. By Stefanie Senger. #28043

• Enhancement preprocessing.MaxAbsScaler can now clip out-of-range values in held-out data with the parameter clip. By Hleb Levitski. #31790

sklearn.tree

• Fix Make tree.export_text thread-safe. By Olivier Grisel. #30041

sklearn.utils

• Efficiency The function linear_model.utils.safe_sparse_dot was improved by a dedicated Cython routine for the case of a @ b with sparse 2-dimensional a and b and when a dense output is required, i.e., dense_output=True. This improves several algorithms in scikit-learn when dealing with sparse arrays (or matrices). By Christian Lorentzen. #31952

• Enhancement sklearn.utils.estimator_checks.parametrize_with_checks now lets you configure strict mode for xfailing checks. Tests that unexpectedly pass will lead to a test failure. The default behaviour is unchanged. By Tim Head. #31951

` |Enhancement|`sklearn.utils._check_sample_weight`` now raises a clearer error message when the provided weights are neither a scalar nor a 1-D array-like of the same size as the input data. #31712 by Kapil Parekh. #31873

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.