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.
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