Version 1.10

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 1.10.dev0

July 2026

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 covariance.LedoitWolf now supports array API compatible inputs. By Tim Head #33573

  Enhancement linear_model.LogisticRegression now supports array API compatible inputs with solver="newton-cg". By Christian Lorentzen and Omar Salman. #34412

• Fix Fixed metrics.median_absolute_error to handle integer inputs when the input array namespace does not support integer inputs to its quantile function (e.g., PyTorch). By Lucy Liu. #33778

Metadata routing

Refer to the Metadata Routing User Guide for more details.

• Fix Metadata is now correctly routed to the sub-estimator of meta-estimators such as feature_selection.SelectFromModel, feature_selection.RFE, feature_selection.RFECV, feature_selection.SequentialFeatureSelector, and impute.IterativeImputer when they are used as an intermediate step of a pipeline.Pipeline, i.e. when their composite fit_transform method is the target of the routing. Previously, metadata such as sample_weight was silently dropped in this case. By Adrin Jalali and Kyle Schluns. #30869

• Fix ensemble.BaggingClassifier now correctly routes metadata via predict and predict_proba dynamically to the corresponding method on the sub-estimator. By Stefanie Senger. #34188

• Fix pipeline.Pipeline’s fit_transform and fit_predict now correctly apply transform_input to metadata routed to intermediate steps, matching fit. By Stefanie Senger. #34201

• Fix compose.TransformedTargetRegressor now correctly routes metadata such as sample_weight to its default regressor (LinearRegression) when metadata routing is enabled. By Stefanie Senger. #34224

sklearn.calibration

• Feature calibration.calibration_curve, calibration.CalibrationDisplay.from_estimator, and calibration.CalibrationDisplay.from_predictions now support n_bins="cube_root". This option sets the number of bins to ceil(n_samples**(1/3)). By Zeyu Sun #33856

sklearn.datasets

• Fix datasets.fetch_openml now correctly recovers from a corrupted download by re-downloading it, instead of raising an error. By Roman Yurchak. #34262

sklearn.ensemble

• Efficiency Part of the fit of sklearn.ensemble.GradientBoostingClassifier was optimized. The speed-up is mainly visible for deeper trees, which is not the primary use case for gradient boosting but can occur in grid searches. For very deep trees, this can be up to 20 times faster. By Arthur Lacote #32911

• Efficiency Fitting in sklearn.ensemble.HistGradientBoostingClassifier and sklearn.ensemble.HistGradientBoostingRegressor was optimized to be faster, by speeding up assignment of bins. By Itamar Turner-Trauring. #34194

• Efficiency Improved the performance of binning in ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor. Fitting with sample weights could be slow for datasets with fewer than 200,000 samples; this is now fixed. Binning without sample weights was also optimized. By Arthur Lacote. #34248

• API Change The sklearn.experimental.enable_hist_gradient_boosting module is deprecated and will be removed in 1.12. It is no longer needed since ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor are stable and can be imported normally from sklearn.ensemble. By Guillaume Lemaitre. #34236

sklearn.impute

• API Change impute.IterativeImputer is no longer experimental. It can now be imported directly with from sklearn.impute import IterativeImputer, and importing sklearn.experimental.enable_iterative_imputer is no longer required (doing so now raises a warning and is a no-op). It also no longer raises a exceptions.ConvergenceWarning when max_iter is reached without meeting the tol stopping criterion, as non-convergence of the round-robin imputation is expected. The User Guide now documents why the imputed values are not guaranteed to converge and why investing in better imputation often yields diminishing returns for prediction. By Guillaume Lemaitre. #34214

sklearn.inspection

• Enhancement The parameter multiclass_colors was deprecated in favour of target_colors in inspection.DecisionBoundaryDisplay. The attribute multiclass_colors_ was also renamed to target_colors_. Now they can be used for binary problems as well without causing confusion (which will be added in a follow-up PR). By Anne Beyer. #34092

sklearn.linear_model

• Efficiency The "newton-cholesky" solver of linear_model.LogisticRegression, linear_model.GammaRegressor, linear_model.PoissonRegressor and linear_model.TweedieRegressor now uses an improved backtracking line search. Instead of halving the step size in each line search iteration, it uses cubic or quadratic polynomial interpolation to find the next trial step size. By Christian Lorentzen. #34157

• Enhancement linear_model.PoissonRegressor, linear_model.GammaRegressor and linear_model.TweedieRegressor got a new solver: solver="newton-cg". This is the same Newton conjugate gradient solver, a.k.a. truncated Newton, that is already available with linear_model.LogisticRegression. By Christian Lorentzen. #33759

• Fix The solver="newton-cg" for linear_model.LogisticRegression, linear_model.PoissonRegressor and other GLMs is now more robust. Many convergence issues have been fixed, e.g. for certain ill-conditioned problems with low regularization and nearly collinear features. The fix is about treating small or even negative curvature. By Christian Lorentzen. #34412

sklearn.metrics

• Efficiency metrics.nan_euclidean_distances is now several times faster on dense data, with a more moderate speed-up for impute.KNNImputer and estimators using metric="nan_euclidean" such as neighbors.NearestNeighbors. By Roman Yurchak. #1140

• Efficiency Scorers created with make_scorer no longer deep-copy metadata kwargs on each call, reducing memory usage during model selection and cross-validation. By Stefanie Senger. #34236

• Fix Fixed metrics.average_precision_score to handle list-type y_score for multiclass data. By Lucy Liu. #34083

• API Change In metrics.DetCurveDisplay the estimator_name parameter is deprecated in favour of name and will be removed in 1.12. The **kwargs parameter of metrics.DetCurveDisplay.plot, metrics.DetCurveDisplay.from_estimator and metrics.DetCurveDisplay.from_predictions is also deprecated in favour of curve_kwargs and will be removed in 1.12. By @AnneBeyer. #34443

sklearn.mixture

• Efficiency mixture.GaussianMixture with covariance_type="tied" is now faster, with the speed-up most visible for many components or high-dimensional data. By Roman Yurchak. #1140

sklearn.model_selection

• API Change model_selection.HalvingGridSearchCV and model_selection.HalvingRandomSearchCV now expose the cross-validation results of the last halving iteration in cv_results_. The full per-iteration history is available in the new all_cv_results_ attribute. By Guillaume Lemaitre. #34250

sklearn.neighbors

• Feature neighbors.NeighborhoodComponentsAnalysis now supports sparse input matrices for init in ['pca', 'random', 'identity']. By Arturo Amor. #34122

• Efficiency neighbors.KNeighborsRegressor.predict is now faster for regression with many output targets. By Roman Yurchak. #1140

• Efficiency sklearn.neighbors.KNeighborsClassifier is now faster when running with multiple threads. The statistics returned from sklearn.neighbors.BallTree and sklearn.neighbors.KDTree no longer reset on each query, and are no longer incorrect when the tree is used from multiple threads. #34187

sklearn.pipeline

• Fix The default value for the transform_input parameter of Pipeline was changed from None to ("X_val",) so that the validation set, when passed to fit, is always transformed alongside X, to prevent easy to miss mistakes. By Jérémie du Boisberranger. #34263

sklearn.preprocessing

• Efficiency Improved fit time of preprocessing.OneHotEncoder and preprocessing.OrdinalEncoder on object/string categorical features when category counts are needed, for instance with min_frequency or max_categories. By Itamar Turner-Trauring. #34386

• Efficiency preprocessing.OneHotEncoder, preprocessing.OrdinalEncoder, and preprocessing.TargetEncoder are now faster by using a Fortran-contiguous memory layout in their encoding paths. By Arthur Lacote. #34392

sklearn.svm

• Fix Fixed a memory leak when calling the fit method of svm.LinearSVR. By Jake VanderPlas. #34256

• Fix Fixed an out-of-bound write in the fit method of svm.LinearSVC memory when using multi_class="crammer_singer". By Jake VanderPlas. #34273

sklearn.tree

• Major Feature tree.DecisionTreeRegressor, tree.DecisionTreeClassifier now have native support for categorical features for binary classification and single-output regression. Categorical features can be specified with the categorical_features parameter. Up to 256 categories per features are supported. By :user:`Adam Li <adam2392>, :user:`Arthur Lacote <cakedev0>, Adrin Jalali and Christian Lorentzen #33354

• Feature Added a fill_colors parameter to tree.plot_tree and tree.export_graphviz to allow user-defined class colors when rendering filled decision trees. By Simon-Martin Schröder. #33810

sklearn.utils

• Fix The HTML representation of estimators now truncates the output feature list to 100 entries to improve rendering performance for estimators with many output features. By Dea María Léon. #34362

• Fix Array API supporting functions and estimators that handle mixed input namespaces now make negative-stride NumPy arrays contiguous before transferring them to PyTorch through DLPack, avoiding a Python process abort in torch.from_dlpack. By Yin Li. #34380

• Fix set_output(transform="polars") no longer emits a DeprecationWarning with polars>=1.42.1 by using how="horizontal_extend" instead of the deprecated how="horizontal" when concatenating polars dataframes. By Felix Hoehle. #34447

Code and documentation contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.9, including:

TODO: update at the time of the release.