Version 1.0.0¶

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`¶

Efficiency The “k-means++” initialization of cluster.KMeans and cluster.MiniBatchKMeans is now faster, especially in multicore settings. #19002 by Jon Crall and Jérémie du Boisberranger.
Efficiency cluster.KMeans with algorithm='elkan' is now faster in multicore settings. #19052 by Yusuke Nagasaka.
Fix Fixes incorrect multiple data-conversion warnings when clustering boolean data. #19046 by Surya Prakash.

`sklearn.feature_extraction`¶

Fix Fixed a bug in class:feature_extraction.HashingVectorizer where some input strings would result in negative indices in the transformed data. #19035 by Liu Yu.

`sklearn.linear_model`¶

Enhancement Validate user-supplied gram matrix passed to linear models via the precompute argument. #19004 by Adam Midvidy.
Fix ElasticNet.fit no longer modifies sample_weight in place. #19055 by Thomas Fan.
Fix Lasso, ElasticNet no longer have a dual_gap_ not corresponding to their objective. #19172 by Mathurin Massias
API Change : The parameter normalize of linear_model.LinearRegression is deprecated and will be removed in 1.2. Motivation for this deprecation: normalize parameter did not take any effect if fit_intercept was set to False and therefore was deemed confusing. The behavior of the deprecated LinearRegression(normalize=True) can be reproduced with Pipeline with :pr:`17743 by Maria Telenczuk and Alexandre Gramfort.

`sklearn.metrics`¶

API Change metrics.ConfusionMatrixDisplay exposes two class methods from_estimator and from_predictions allowing to create a confusion matrix plot using an estimator or the predictions. metrics.plot_confusion_matrix is deprecated in favor of these two class methods and will be removed in 1.2. #18543 by Guillaume Lemaitre.

`sklearn.naive_bayes`¶

Fix The fit and partial_fit methods of the discrete naive Bayes classifiers (naive_bayes.BernoulliNB, naive_bayes.CategoricalNB, naive_bayes.ComplementNB, and naive_bayes.MultinomialNB) now correctly handle the degenerate case of a single class in the training set. #18925 by David Poznik.
API Change The attribute sigma_ is now deprecated in naive_bayes.GaussianNB and will be removed in 1.2. Use var_ instead. #18842 by Hong Shao Yang.

`sklearn.preprocessing`¶

Feature The new preprocessing.SplineTransformer is a feature preprocessing tool for the generation of B-splines, parametrized by the polynomial degree of the splines, number of knots n_knots and knot positioning strategy knots. #18368 by Christian Lorentzen.

`sklearn.tree`¶

Enhancement Add fontname argument in tree.export_graphviz for non-English characters. #18959 by Zero and wstates.

Code and Documentation Contributors¶

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.24, including:

TODO: update at the time of the release.

Version 1.0.0¶

Legend for changelogs¶

Changed models¶

Changelog¶

sklearn.cluster¶

sklearn.feature_extraction¶

sklearn.linear_model¶

sklearn.metrics¶

sklearn.naive_bayes¶

sklearn.preprocessing¶

sklearn.tree¶