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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