pystacked: Stacking generalization and machine learning in Stata

Achim Ahrens, Christian Hansen and Mark Schaffer ()

Stata Journal, 2023, vol. 23, issue 4, 909-931

Abstract: The pystacked command implements stacked generalization (Wolpert, 1992, Neural Networks 5: 241–259) for regression and binary classification via Python’s scikit-learn. Stacking combines multiple supervised machine learners— the “base” or “level-0” learners—into one 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 one base learner and thus provides an easy-to-use application programming interface for scikit-learn’s machine learning algorithms.

Keywords: pystacked; machine learning; stacked generalization; model averaging; Python; sci-kit learn (search for similar items in EconPapers)
Date: 2023
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Working Paper: pystacked: Stacking generalization and machine learning in Stata (2023)
Working Paper: pystacked: Stacking generalization and machine learning in Stata (2022)
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DOI: 10.1177/1536867X231212426

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