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Train sklearn 100x faster

I'm excited to announce the launch of the open source project sk-dist.


The goal of the project is to provide a general framework for distributing scikit-learn meta-estimators with Spark.


Examples of meta-estimators are decision tree ensembles (random forests and extra randomized trees), hyperparameter tuners (grid search and randomized search) and multi-class techniques (one vs rest and one vs one).


To get started with sk-dist, check out the link bellow. The code repository also contains a library of examples to illustrate some of the use cases of sk-dist. All are welcome to submit issues and contribute to the project.



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