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#

August 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.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

`sklearn.calibration`#

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

`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.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 and linear_model.LassoCV with precompute=True (or precompute="auto"` and n_samples > n_features) are faster to fit by avoiding a BLAS level 1 (axpy) call in the inner most loop. Same for functions linear_model.enet_path and linear_model.lasso_path. By Christian Lorentzen. #31880
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" as well as the new parameter PA_C for the aggressiveness parameter of the Passive-Aggressive-Algorithms. By Christian Lorentzen. #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

` |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.