Version 1.9

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 1.9.dev0

February 2026

Changes impacting many modules

• Enhancement pipeline.Pipeline, pipeline.FeatureUnion and compose.ColumnTransformer now raise a clearer error message when an estimator class is passed instead of an instance. By Anne Beyer #32888

• Fix Raise ValueError when sample_weight contains only zero values to prevent meaningless input data during fitting. This change applies to all estimators that support the parameter sample_weight. This change also affects metrics that validate sample weights. By Lucy Liu and John Hendricks. #32212

• Fix Some parameter descriptions in the HTML representation of estimators were not properly escaped, which could lead to malformed HTML if the description contains characters like < or >. By Olivier Grisel. #32942

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.d2_absolute_error_score and sklearn.metrics.d2_pinball_score now support array API compatible inputs. By Virgil Chan. #31671

• Feature sklearn.metrics.ranking.average_precision_score now supports Array API compliant inputs. By Stefanie Senger. #32909

• Feature sklearn.metrics.pairwise.paired_manhattan_distances now supports array API compatible inputs. By Bharat Raghunathan. #32979

• Feature sklearn.metrics.pairwise.pairwise_distances_argmin now supports array API compatible inputs. By Bharat Raghunathan. #32985

• Enhancement kernel_approximation.Nystroem now supports array API compatible inputs. By Emily Chen #29661

• Fix Fixed a bug that would cause Cython-based estimators to fail when fit on NumPy inputs when setting sklearn.set_config(array_api_dispatch=True). By Olivier Grisel. #32846

• Fix Fixes how pos_label is inferred when pos_label is set to None, in sklearn.metrics.brier_score_loss and sklearn.metrics.d2_brier_score. By Lucy Liu. #32923

Metadata routing

Refer to the Metadata Routing User Guide for more details.

• Enhancement TargetEncoder now routes groups to the CV splitter internally used for cross fitting in its fit_transform. By Samruddhi Baviskar and Stefanie Senger. #33089

sklearn.cluster

• Fix cluster.MiniBatchKMeans now correctly handles sample weights during fitting. When sample weights are not None, mini-batch indices are created by sub-sampling with replacement using the normalized sample weights as probabilities. By Shruti Nath, Olivier Grisel, and Jeremie du Boisberranger. #30751

sklearn.compose

• Fix The dotted line for compose.ColumnTransformer in its HTML display now includes only its elements. The behaviour when a remainder is used, has also been corrected. By Dea María Léon #32713

sklearn.datasets

• Efficiency Re-enabled compressed caching for datasets.fetch_kddcup99, reducing on-disk cache size without changing the public API. By Unique Shrestha. #33118

sklearn.ensemble

• Fix ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier and ensemble.ExtraTreesRegressor now use sample_weight to draw the samples instead of forwarding them multiplied by a uniformly sampled mask to the underlying estimators. Furthermore, when max_samples is a float, it is now interpreted as a fraction of sample_weight.sum() instead of X.shape[0]. As sampling is done with replacement, a float max_samples greater than 1.0 is now allowed, as well as an integer max_samples greater then X.shape[0]. The default max_samples=None draws X.shape[0] samples, irrespective of sample_weight. By Antoine Baker. #31529

• Fix Both ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier with the default "friedman_mse" criterion were computing impurity values with an incorrect scaling, leading to unexpected trees in some cases. The implementation now uses "squared_error", which is exactly equivalent to "friedman_mse" up to floating-point error discrepancies but computes correct impurity values. By Arthur Lacote. #32708

• API Change The criterion parameter is now deprecated for classes ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier, as both options ("friedman_mse" and "squared_error") were producing the same results, up to floating-point rounding discrepancies and a bug in "friedman_mse". By Arthur Lacote #32708

sklearn.inspection

• Fix In inspection.DecisionBoundaryDisplay, multiclass_colors is now also used for multiclass plotting when response_method="predict". By Anne Beyer. #33015

• Fix In inspection.DecisionBoundaryDisplay, n_classes is now inferred more robustly from the estimator. If it fails for custom estimators, a comprehensive error message is shown. By Anne Beyer. #33202

sklearn.linear_model

• Enhancement linear_model.ElasticNet, linear_model.ElasticNetCV and linear_model.enet_path now are able to fit Ridge regression, i.e. setting l1_ratio=0. Before this PR, the stopping criterion was a formulation of the dual gap that breaks down for l1_ratio=0. Now, an alternative dual gap formulation is used for this setting. This reduces the noise of raised warnings. By Christian Lorentzen. #32845

• Enhancement Efficiency linear_model.ElasticNet, linear_model.ElasticNetCV, linear_model.Lasso, linear_model.LassoCV, linear_model.MultiTaskElasticNet, linear_model.MultiTaskElasticNetCV linear_model.MultiTaskLasso, linear_model.MultiTaskLassoCV as well as linear_model.lasso_path and linear_model.enet_path are now faster when fit with strong L1 penalty and many features. During gap safe screening of features, the update of the residual is now only performed if the coefficient is not zero. By Christian Lorentzen. #33161

• Fix linear_model.LassoCV and linear_model.ElasticNetCV now take the positive parameter into account to compute the maximum alpha parameter, where all coefficients are zero. This impacts the search grid for the internally tuned alpha hyper-parameter stored in the attribute alphas_. By Junteng Li #32768

• Fix Correct the formulation of alpha within linear_model.SGDOneClassSVM. The corrected value is alpha = nu instead of alpha = nu / 2. Note: This might result in changed values for the fitted attributes like coef_ and offset_ as well as the predictions made using this class. By Omar Salman. #32778

• Fix linear_model.enet_path now correctly handles the precompute parameter when check_input=False. Previously, the value of precompute was not properly treated which could lead to a ValueError. This also affects linear_model.ElasticNetCV, linear_model.LassoCV, linear_model.MultiTaskElasticNetCV and linear_model.MultiTaskLassoCV. By Albert Dorador #33014

• Fix Fixed a bug in linear_model.SGDClassifier for multiclass settings where large negative values of :method:`decision_function` could lead to NaN values. In this case, this fix assigns equal probability for each class. By Christian Lorentzen. #33168

sklearn.metrics

• Enhancement cohen_kappa_score now has a replace_undefined_by param, that can be set to define the function’s return value when the metric is undefined (division by zero). By Stefanie Senger #31172

• Fix metrics.d2_pinball_score and metrics.d2_absolute_error_score now always use the "averaged_inverted_cdf" quantile method, both with and without sample weights. Previously, the "linear" quantile method was used only for the unweighted case leading the surprising discrepancies when comparing the results with unit weights. Note that all quantile interpolation methods are asymptotically equivalent in the large sample limit, but this fix can cause score value changes on small evaluation sets (without weights). By Virgil Chan. #31671

sklearn.pipeline

• Fix Fixed pipeline.FeatureUnion to properly handle column renaming when using Polars output, preventing duplicate column names. By Levente Csibi. #32853 #32853

sklearn.svm

• Fix Raise more informative error when fitting NuSVR with all zero sample weights. By Lucy Liu and John Hendricks. #32212

sklearn.tree

• Fix Fixed feature-wise NaN detection in trees. Features could be seen as NaN-free for some edge-case patterns, which led to not considering splits with NaNs assigned to the left node for those features. This affects: - tree.DecisionTreeRegressor - tree.ExtraTreeRegressor - ensemble.RandomForestRegressor - ensemble.ExtraTreesRegressor By Arthur Lacote #32193

• API Change criterion="friedman_mse" is now deprecated. This criterion was intended for gradient boosting but was incorrectly implemented in scikit-learn’s trees and was actually behaving identically to criterion="squared_error". Use criterion="squared_error" instead. This affects: - tree.DecisionTreeRegressor - tree.ExtraTreeRegressor - ensemble.RandomForestRegressor - ensemble.ExtraTreesRegressor By Arthur Lacote #32708

sklearn.utils

• Enhancement sklearn.utils._tags.get_tags now provides a clearer error message when a class is passed instead of an estimator instance. By Achyuthan S and Anne Beyer. #32565

• Enhancement sklearn.utils._response._get_response_values now provides a clearer error message when estimator does not implement the given response_method. By Quentin Barthélemy. #33126

• Fix The parameter table in the HTML representation of all scikit-learn estimators inheritiging from base.BaseEstimator, displays each parameter documentation as a tooltip. The last tooltip of a parameter in the last table of any HTML representation was partially hidden. This issue has been fixed. By Dea María Léon #32887

• Fix Fixed _weighted_percentile with average=True so zero-weight samples

  just before the end of the array are handled correctly. This

  can change results when using sample_weight with preprocessing.KBinsDiscretizer (strategy="quantile", quantile_method="averaged_inverted_cdf") and in metrics.median_absolute_error, metrics.d2_pinball_score, and metrics.d2_absolute_error_score. By Arthur Lacote. #33127

Code and documentation contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.8, including:

TODO: update at the time of the release.