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pystacked: Stacking generalization and machine learning in Stata

Author

Listed:
  • Achim Ahrens

    (ETH Zürich)

  • Christian B. Hansen

    (University of Chicago)

  • Mark E. Schaffer

    (Heriot-Watt University)

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.

Suggested Citation

  • Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2023. "pystacked: Stacking generalization and machine learning in Stata," Stata Journal, StataCorp LLC, vol. 23(4), pages 909-931, December.
    • Handle: RePEc:tsj:stataj:v:23:y:2023:i:4:p:909-931
    DOI: 10.1177/1536867X231212426
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    Cited by:

    1. Marcos Delprato, 2025. "Identifying the post-pandemic determinants of low performing students in Latin America through Interpretable Machine Learning methods," Papers 2509.24508, arXiv.org, revised Mar 2026.
    2. Zuchuat, Jeremy & Lalive, Rafael & Osikominu, Aderonke & Pesaresi, Lorenzo & Zweimüller, Josef, 2023. "Duration Dependence in Finding a Job: Applications, Interviews, and Job Offers," CEPR Discussion Papers 18600, C.E.P.R. Discussion Papers.
    3. Bigler, Patrick, 2025. "Magnitude and decomposition of the solar rebound: Evidence from Swiss households," Journal of Environmental Economics and Management, Elsevier, vol. 133(C).
    4. Nicolas Apfel & Holger Breinlich & Nick Green & Dennis Novy & J. M. C. Santos Silva & Tom Zylkin, 2025. "Out-of-sample gravity predictions and trade policy counterfactuals," Papers 2509.11271, arXiv.org, revised Apr 2026.
    5. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2025. "Model Averaging and Double Machine Learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 249-269, April.
    6. Bonaccolto-Töpfer, Marina & Satlukal, Sascha, 2024. "Gender differences in reservation wages: New evidence for Germany," Labour Economics, Elsevier, vol. 91(C).
    7. Philipp Bach & Oliver Schacht & Victor Chernozhukov & Sven Klaassen & Martin Spindler, 2024. "Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study," Papers 2402.04674, arXiv.org.

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