RFE (Recursive Feature Elimination) with cross validation

RFECV from scikit-learn optimizes the number of best features [1] but is usual the desire to know the selected variables for other cases. The RFE function [2] addresses this issue but is it not cross-validated.

A cross-validation for feature importance can give the idea of how a given feature is well established in the feature importance rank. The rank variability is shown by the standard deviation of the rank mean. As the next figure shows:

Figure: Mean rank across folds for each feature.

TO-DO:

• Separate module file and example script;
• Prettier output or returning of results;
• Input parameters for figures (done for figsize);
• Get rid of commentaries in portuguese (on the way);
• Return of figures instead of plotting;
• Better check of inputs (types);
• Consider the variability of the std deviation of the mean;
• Richer example.

[1] http:https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html

[2] https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html