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

June 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

Metadata routing

Refer to the Metadata Routing User Guide for more details.

• 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

• 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

• 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

sklearn.metrics

• 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

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 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.svm

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

sklearn.tree

• 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

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.