[scikit-learn] Release Candidate for Scikit-learn 0.21

[Andreas Mueller]

Thank you for all the amazing work y'all!


On 4/30/19 10:09 PM, Joel Nothman wrote:
> 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.
>
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> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn

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