pystacked: Stacking generalization and machine learning in Stata
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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.
Key facts. Cited by 1 paper 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
3 of 40 references.
References · 3
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019Machine Learning Methods Economists Should Know Aboutcited by 64
- 2021Machine Learning using Stata/Pythoncited by 2
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Citing papers per year: 2023: 1.
1 paper.
- 2023ddml: Double/debiased machine learning in Statacited by 1
- 2023ddml: Double/debiased machine learning in Stata23 Jan 2023