I'm happy to announce ScikitLearn.jl <https://github.com/cstjean/ScikitLearn.jl> and ScikitLearnBase.jl <https://github.com/cstjean/ScikitLearnBase.jl>: a library pair that brings the scikit-learn interface to Julia.
*Highlights* - Around 150 machine learning and statistical models accessed through a uniform interface - Pipelines and FeatureUnions - Cross-validation - Model selection (hyperparameter tuning) - Feature extraction (text processing, one hot encoding, etc.) Check out the documentation <http://scikitlearnjl.readthedocs.org/en/latest/>, quick start guide <http://scikitlearnjl.readthedocs.org/en/latest/quickstart/> and example gallery <https://github.com/cstjean/ScikitLearn.jl/blob/master/docs/examples.md>. ScikitLearn.jl uses PyCall.jl for Python models, but the "glue" (eg. Pipelines) was translated to make it possible to implement models in Julia. For instance, one might pipeline a factor analysis model from Python into a deep learning model written in Julia, and use grid search to optimize its hyperparameters. Any Julia type that implements the scikit-learn interface <https://github.com/cstjean/ScikitLearnBase.jl> can be used with this framework. If you have any issue supporting the interface for your library, ping me @cstjean. Best, Cédric St-Jean
