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pystacked: Stacking generalization and machine learning in Stata

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  • Achim Ahrens
  • Christian B. Hansen
  • Mark E. Schaffer

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 (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.

Suggested Citation

  • Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2022. "pystacked: Stacking generalization and machine learning in Stata," Papers 2208.10896, arXiv.org, revised Mar 2023.
    • Handle: RePEc:arx:papers:2208.10896
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    References listed on IDEAS

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    1. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Vanya Van Belle & Ben Van Calster & Sabine Van Huffel & Johan A K Suykens & Paulo Lisboa, 2016. "Explaining Support Vector Machines: A Color Based Nomogram," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-33, October.
    4. Nick Guenther & Matthias Schonlau, 2016. "Support vector machines," Stata Journal, StataCorp LP, vol. 16(4), pages 917-937, December.
    5. Giovanni Cerulli, 2022. "Machine learning using Stata/Python," Stata Journal, StataCorp LP, vol. 22(4), pages 772-810, December.
    6. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    7. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    8. Kelley Pace, R. & Barry, Ronald, 1997. "Sparse spatial autoregressions," Statistics & Probability Letters, Elsevier, vol. 33(3), pages 291-297, May.
    9. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
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    Cited by:

    1. Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2024. "Model Averaging and Double Machine Learning," IZA Discussion Papers 16714, Institute of Labor Economics (IZA).
    2. 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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