pystacked: Stacking generalization and machine learning in Stata

Author:

Ahrens Achim1,Hansen Christian B.2,Schaffer Mark E.3

Affiliation:

1. ETH Zürich, Zürich, Switzerland,

2. University of Chicago, Chicago, IL,

3. Heriot-Watt University, Edinburgh, U.K.,

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.

Publisher

SAGE Publications

Subject

Mathematics (miscellaneous)

Reference25 articles.

1. lassopack: Model selection and prediction with regularized regression in Stata

2. ddml: Double/Debiased Machine Learning in Stata

3. Ahrens A., Hansen C. B., Schaffer M. E., Wiemann T. Forthcoming. ddml: Double/debiased machine learning in Stata. Stata Journal.

4. Machine Learning Methods That Economists Should Know About

5. Stacked regressions

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