Version 0.23.0

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.

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.cluster

sklearn.compose

sklearn.datasets

sklearn.decomposition

sklearn.ensemble

sklearn.feature_extraction

sklearn.gaussian_process

sklearn.impute

sklearn.linear_model

sklearn.metrics

sklearn.model_selection

sklearn.multioutput

sklearn.naive_bayes

sklearn.neural_network

sklearn.preprocessing

sklearn.svm

sklearn.tree

sklearn.utils

sklearn.cluster

Miscellaneous

  • API Change Most estimators now expose a n_features_in_ attribute. This attribute is equal to the number of features passed to the fit method. See SLEP010 for details. #16112 by Nicolas Hug.