Version 1.7

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 1.7.dev0

January 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.utils.check_consistent_length now supports Array API compatible inputs. By Stefanie Senger #29519

• Feature sklearn.metrics.explained_variance_score and sklearn.metrics.mean_pinball_loss now support Array API compatible inputs. By Virgil Chan #29978

• Feature sklearn.metrics.fbeta_score, sklearn.metrics.precision_score and sklearn.metrics.recall_score now support Array API compatible inputs. By Omar Salman #30395

sklearn.datasets

• Enhancement New parameter return_X_y added to datasets.make_classification. The default value of the parameter does not change how the function behaves. By Success Moses and Adam Cooper #30196

sklearn.decomposition

• Feature DictionaryLearning, SparseCoder and MiniBatchDictionaryLearning now have a inverse_transform method. By Rémi Flamary #30443

sklearn.inspection

• Enhancement Add custom_values parameter in inspection.partial_dependence. It enables users to pass their own grid of values at which the partial dependence should be calculated. By Freddy A. Boulton and Stephen Pardy #26202

• API Change inspection.partial_dependence does no longer accept integer dtype for numerical feature columns. Explicity conversion to floating point values is now required before calling this tool (and preferably even before fitting the model to inspect). By Olivier Grisel #30409

sklearn.linear_model

• Fix Enhancement Added a new paramenter tol to

  linear_model.LinearRegression that determines the precision of the solution coef_ when fitting on sparse data. By Success Moses #30521

sklearn.metrics

• Fix metrics.RocCurveDisplay will no longer set a legend when label is None in both the line_kwargs and the chance_level_kw. By Arturo Amor #29727

sklearn.mixture

• Efficiency Simplified redundant computation when estimating covariances in GaussianMixture with a covariance_type="spherical" or covariance_type="diag". By Leonce Mekinda and Olivier Grisel #30414

sklearn.multioutput

• Enhancement The parameter base_estimator has been deprecated in favour of estimator for multioutput.RegressorChain and multioutput.ClassifierChain. By Success Moses and dikraMasrour #30152

sklearn.neural_network

• Feature Added support for sample_weight in neural_network.MLPClassifier and neural_network.MLPRegressor. By Zach Shu and Christian Lorentzen #30155

sklearn.pipeline

• Enhancement Expose the verbose_feature_names_out argument in the pipeline.make_union function, allowing users to control feature name uniqueness in the pipeline.FeatureUnion. By Abhijeetsingh Meena #30406

sklearn.utils

• Enhancement Warning filters from the main process are propagated to joblib workers. By Thomas Fan #30380

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.