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
andcluster.MiniBatchKMeans
is now faster, especially in multicore settings. #19002 by Jon Crall and Jérémie du Boisberranger.Efficiency
cluster.KMeans
withalgorithm='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.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 modifiessample_weight
in place. #19055 by Thomas Fan.Fix
Lasso
,ElasticNet
no longer have adual_gap_
not corresponding to their objective. #19172 by Mathurin MassiasAPI Change : The parameter
normalize
oflinear_model.LinearRegression
is deprecated and will be removed in 1.2. Motivation for this deprecation:normalize
parameter did not take any effect iffit_intercept
was set to False and therefore was deemed confusing. The behavior of the deprecated LinearRegression(normalize=True) can be reproduced withPipeline
with:pr:`17743
by Maria Telenczuk and Alexandre Gramfort.
sklearn.metrics
¶
API Change
metrics.ConfusionMatrixDisplay
exposes two class methodsfrom_estimator
andfrom_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
andpartial_fit
methods of the discrete naive Bayes classifiers (naive_bayes.BernoulliNB
,naive_bayes.CategoricalNB
,naive_bayes.ComplementNB
, andnaive_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 innaive_bayes.GaussianNB
and will be removed in 1.2. Usevar_
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 polynomialdegree
of the splines, number of knotsn_knots
and knot positioning strategyknots
. #18368 by Christian Lorentzen.
sklearn.tree
¶
Enhancement Add
fontname
argument intree.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.