Indeed! Great improvements. And it's a pleasure to see that the releases are more frequent: a huge value to the community.
Gaël On Thu, May 16, 2019 at 10:21:09AM +0200, bertrand.thirion wrote: > Congratulations ! > Bertrand > Envoyé depuis mon smartphone Samsung Galaxy. > -------- Message d'origine -------- > De : Joel Nothman <joel.noth...@gmail.com> > Date : 16/05/2019 10:03 (GMT+01:00) > À : Scikit-learn user and developer mailing list <scikit-learn@python.org> > Objet : [scikit-learn] ANN: scikit-learn 0.21 released > Thanks to the work of many, many contributors, we have released Scikit-learn > 0.21. It is available from GitHub, PyPI and Conda-forge, but is not yet > available on the Anaconda defaults channel. > * 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/ > * Install from conda-forge with `conda install -c conda-forge scikit-learn` > 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: an experimental API for 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. It requires: > * 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) > * Python >= 3.5. Installing Scikit-learn from Python 2 will continue to > provide > version 0.20. > Thanks again to everyone who contributed and to our sponsors, who helped us to > develop such a great set of features and fixes since version 0.20 in under 8 > months. > Happy Learning! > From the Scikit-learn ]team. > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn -- Gael Varoquaux Senior Researcher, INRIA http://gael-varoquaux.info http://twitter.com/GaelVaroquaux _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
