  We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting
popular Machine Learning (ML) methods both in regression and classification
settings. Using the recent Stata/Python integration platform (sfi) of Stata 16,
these commands provide hyper-parameters' optimal tuning via K-fold
cross-validation using greed search. More specifically, they make use of the
Python Scikit-learn API to carry out both cross-validation and outcome/label
prediction.
