Reading the Scikit-Learn Documentation.

Relationship to scikit-learn

ScikitLearn.jl aims to mirror the Python scikit-learn project, but the API had to be adapted to Julia, and follows Julia's conventions. When reading the Python documentation, keep in mind:

• Most object methods are now functions: Python's model.predict(X) becomes predict(model, X)
• Methods that modify the model's state have a ! at the end: model.fit_transform(X) becomes fit_transform!(model, X)
• A few of the Python submodules were translated into Julia to support Julia models: ScikitLearn.Pipelines, ScikitLearn.CrossValidation, and ScikitLearn.GridSearch

You can access the class members and methods of a Python object (i.e. all models imported through @sk_import) using obj.member_name. For example:

julia> X=rand(10,4);y=rand(10);

julia> @sk_import linear_model: Lasso
PyObject <class 'sklearn.linear_model._coordinate_descent.Lasso'>

julia> lm = fit!(Lasso(), X, y)
PyObject Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,
      normalize=False, positive=False, precompute=False, random_state=None,
      selection='cyclic', tol=0.0001, warm_start=False)

julia> println(lm.n_iter_)
1

This is rarely necessary, because the most important/frequently-used methods have been defined in Julia (eg. transformer.classes_ is now get_classes(transformer))