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-tagsThere 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.
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