# Machine Learning using Stata/Python

arXiv: https://arxiv.org/abs/2103.03122 · HTML: https://mlsanity.com/paper/2103.03122/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)

## Built on by (most cited first, 2 of 2)

- [pystacked: Stacking generalization and machine learning in Stata](https://mlsanity.com/paper/2208.10896/) (2022)
- [A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning](https://mlsanity.com/paper/2407.14695/) (2024)

## Abstract (arXiv)

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.