Version 0.24.0

In Development

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 documentated – 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.

Put the changes in their relevant module.

Changed models

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

  • item

  • item

Details are listed in the changelog below.

(While we are trying to better inform users by providing this information, we cannot assure that this list is complete.)

Changelog

sklearn.calibration

sklearn.calibrator

  • Enhancement Allow calibrator.CalibratedClassifierCV use with prefit pipeline.Pipeline where data is not X is not array-like, sparse matrix or dataframe at the start. #17546 by Lucy Liu.

sklearn.cluster

sklearn.compose

sklearn.covariance

  • API Change Deprecates cv_alphas_ in favor of cv_results['alphas'] and grid_scores_ in favor of split scores in cv_results_ in covariance.GraphicalLassoCV. cv_alphas_ and grid_scores_ will be removed in version 0.26. #16392 by Thomas Fan.

sklearn.datasets

sklearn.decomposition

  • Fix Fix decomposition.SparseCoder such that it follows scikit-learn API and support cloning. The attribute components_ is deprecated in 0.24 and will be removed in 0.26. This attribute was redundant with the dictionary attribute and constructor parameter. #17679 by Xavier Dupré.

  • Enhancement decomposition.FactorAnalysis now supports the optional argument rotation, which can take the value None, 'varimax' or 'quartimax'. #11064 by Jona Sassenhagen.

  • Enhancement decomposition.NMF now supports the optional parameter regularization, which can take the values None, components, transformation or both, in accordance with decomposition.NMF.non_negative_factorization. #17414 by Bharat Raghunathan.

sklearn.ensemble

sklearn.exceptions

  • API Change exceptions.ChangedBehaviorWarning and exceptions.NonBLASDotWarning are deprecated and will be removed in v0.26, #17804 by Adrin Jalali.

sklearn.feature_extraction

sklearn.feature_selection

sklearn.gaussian_process

  • Enhancement A new method gaussian_process.Kernel._check_bounds_params is called after fitting a Gaussian Process and raises a ConvergenceWarning if the bounds of the hyperparameters are too tight. #12638 by Sylvain Lannuzel

sklearn.impute

sklearn.inspection

sklearn.isotonic

sklearn.linear_model

sklearn.manifold

  • Enhancement Add square_distances parameter to manifold.TSNE, which provides backward compatibility during deprecation of legacy squaring behavior. Distances will be squared by default in 0.26, and this parameter will be removed in 0.28. #17662 by Joshua Newton.

  • Efficiency Fixed #10493. Improve Local Linear Embedding (LLE) that raised MemoryError exception when used with large inputs. #17997 by Bertrand Maisonneuve.

sklearn.metrics

sklearn.model_selection

sklearn.multiclass

sklearn.naive_bayes

sklearn.neighbors

sklearn.neural_network

sklearn.preprocessing

sklearn.svm

sklearn.tree

Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.20, including: