pystacked: Stacking generalization and machine learning in Stata

• URL: http://arxiv.org/abs/2208.10896v1
• Date: Tue, 23 Aug 2022 12:03:04 GMT
• Title: pystacked: Stacking generalization and machine learning in Stata
• Authors: Achim Ahrens, Christian B. Hansen, Mark E. Schaffer
• Abstract summary: pystacked implements stacked generalization via Python's scikit-lear. Stacking combines multiple supervised machine learners into a single learner. pystacked provides an easy-to-use API for scikit-learn's machine learning algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python's scikit-lear}. 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.

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