pystacked: Stacking generalization and machine learning in Stata

Achim Ahrens, Christian Hansen and Mark Schaffer ()

Papers from arXiv.org

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.

Date: 2022-08, Revised 2023-03
New Economics Papers: this item is included in nep-big and nep-cmp
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Journal Article: pystacked: Stacking generalization and machine learning in Stata (2023)
Working Paper: pystacked: Stacking generalization and machine learning in Stata (2022)
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