PyPI now has source and binary releases for Scikit-learn 0.21rc2.

* Documentation at https://scikit-learn.org/0.21

* Release Notes at https://scikit-learn.org/0.21/whats_new

* Download source or wheels at https://pypi.org/project/scikit-learn/0.21rc2/

Please try out the software and help us edit the release notes before a final release.

Highlights include:

* neighbors.NeighborhoodComponentsAnalysis for supervised metric learning, which learns a weighted euclidean distance for k-nearest neighbors. https://scikit-learn.org/0.21/modules/neighbors.html#nca

* ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor: experimental implementations of efficient binned gradient boosting machines. https://scikit-learn.org/0.21/modules/ensemble.html#gradient-tree-boosting

* impute.IterativeImputer: a non-trivial approach to missing value imputation. https://scikit-learn.org/0.21/modules/impute.html#multivariate-feature-imputation

* cluster.OPTICS: a new density-based clustering algorithm. https://scikit-learn.org/0.21/modules/clustering.html#optics

* better printing of estimators as strings, with an option to hide default parameters for compactness: https://scikit-learn.org/0.21/auto_examples/plot_changed_only_pprint_parameter.html

* for estimator and library developers: a way to tag your estimator so that it can be treated appropriately with check_estimator. https://scikit-learn.org/0.21/developers/contributing.html#estimator-tags

There are many other enhancements and fixes listed in the release notes (https://scikit-learn.org/0.21/whats_new).

Please note that Scikit-learn has new dependencies:

* joblib >= 0.11, which used to be vendored within Scikit-learn

* OpenMP, unless the environment variable SKLEARN_NO_OPENMP=1 when the code is compiled (and cythonized)

Happy Learning!

From the Scikit-learn core dev team.