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.
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.
multiclass.OutputCodeClassifier.predictnow uses a more efficient pairwise distance reduction. As a consequence, the tie-breaking strategy is different and thus the predicted labels may be different. #25196 by Guillaume Lemaitre.
decomposition.DictionaryLearningis more efficient but may produce different results as in previous versions when
transform_algorithmis not the same as
fit_algorithmand the number of iterations is small. #24871 by Omar Salman.
Changes impacting all modules¶
get_feature_names_outmethod of the following classes now raises a
NotFittedErrorif the instance is not fitted. This ensures the error is consistent in all estimators with the
NotFittedErrordisplays an informative message asking to fit the instance with the appropriate arguments.
ensemble.HistGradientBoostingRegressornow supports the Gamma deviance loss via
loss="gamma". Using the Gamma deviance as loss function comes in handy for modelling skewed distributed, strictly positive valued targets. #22409 by Christian Lorentzen.
Feature Compute a custom out-of-bag score by passing a callable to
ensemble.ExtraTreesRegressor. #25177 by Tim Head.
ensemble.IsolationForestpredict time is now faster (typically by a factor of 8 or more). Internally, the estimator now precomputes decision path lengths per tree at
fittime. It is therefore not possible to load an estimator trained with scikit-learn 1.2 to make it predict with scikit-learn 1.3: retraining with scikit-learn 1.3 is required. #25186 by Felipe Breve Siola.
pipeline.FeatureUnioncan now access the
feature_names_in_attribute if the
Xvalue seen during
columnsattribute and all columns are strings. e.g. when
pandas.DataFrame#25220 by Ian Thompson.
Enhancement Adds a
preprocessing.OneHotEncoder. This specifies a custom callable to create feature names to be returned by
get_feature_names_out. The callable combines input arguments
(input_feature, category)to a string. #22506 by Mario Kostelac.
Enhancement Added support for
preprocessing.KBinsDiscretizer. This allows specifying the parameter
sample_weightfor each sample to be used while fitting. The option is only available when
strategyis set to
kmeans. #24935 by Seladus, Guillaume Lemaitre, and Dea María Léon, #25257 by Gleb Levitski.
Code and Documentation Contributors¶
Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.2, including:
TODO: update at the time of the release.