[scikit-learn] ANN: scikit-learn 0.21 released

Joel Nothman joel.nothman at gmail.com
Thu May 16 04:03:23 EDT 2019

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
* 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.
* ensemble.HistGradientBoostingClassifier
and ensemble.HistGradientBoostingRegressor: experimental implementations of
efficient binned gradient boosting machines. https://scikit-learn.org/0.21
* impute.IterativeImputer: an experimental API for a non-trivial approach
to missing value imputation. https://scikit-learn.org/0.21
* cluster.OPTICS: a new density-based clustering algorithm.
* better printing of estimators as strings, with an option to hide default
parameters for compactness: https://scikit-learn.org/0.21
* for estimator and library developers: a way to tag your estimator so that
it can be treated appropriately with check_estimator.

There are many other enhancements and fixes listed in the release notes (

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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