pystacked: Stacking generalization and machine learning in Stata

Christian Hansen, Mark Schaffer () and Achim Ahrens

Swiss Stata Conference 2022 from Stata Users Group

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 (multilayer perceptron). pystacked can also be used 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: 2022-11-30
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (3)

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http://repec.org/csug2022/Ahrens-Bern2022-pystacked.pdf

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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 (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:csug22:01

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