pystacked: Stacking generalization and machine learning in Stata

TL;DR

pystacked is a Python package that implements stacking ensemble methods for regression and classification, integrating multiple machine learning models like random forests and neural networks for improved predictive performance.

Contribution

It provides an easy-to-use interface in Stata for stacking multiple scikit-learn models, combining diverse learners into a single ensemble for regression and classification tasks.

Findings

Enables stacking of various scikit-learn models in Stata.

Supports multiple base learners including neural nets and gradient boosting.

Facilitates improved predictive accuracy through ensemble learning.

Abstract

pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python's scikit-learn. Stacking combines multiple supervised machine learners -- the "base" or "level-0" learners -- into a single learner. The currently supported base learners include regularized regression, random forest, gradient boosted trees, support vector machines, and feed-forward neural nets (multi-layer perceptron). pystacked can also be used with as a `regular' machine learning program to fit a single base learner and, thus, provides an easy-to-use API for scikit-learn's machine learning algorithms.