# pystacked: Stacking generalization and machine learning in Stata

arXiv: https://arxiv.org/abs/2208.10896 · HTML: https://mlsanity.com/paper/2208.10896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Machine Learning using Stata/Python](https://mlsanity.com/paper/2103.03122/) (2021)

## Built on by (most cited first, 1 of 1)

- [ddml: Double/debiased machine learning in Stata](https://mlsanity.com/paper/2301.09397/) (2023)

## Abstract (arXiv)

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.