Machine Learning using Stata/Python

TL;DR

This paper introduces two Stata modules that leverage Python's Scikit-learn to perform hyper-parameter tuning and cross-validation for machine learning models within Stata, streamlining ML workflows for regression and classification.

Contribution

The paper presents new Stata modules that integrate with Python's Scikit-learn, enabling efficient hyper-parameter tuning and cross-validation in Stata using the sfi platform.

Findings

Effective integration of Stata and Python for ML tasks.

Automated hyper-parameter tuning via cross-validation.

Supports both regression and classification models.

Abstract

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.