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Machine learning using Stata/Python

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  • Giovanni Cerulli

    (IRCrES-CNR)

Abstract

I present two related commands, r_ml_stata_cv and c_ml_stata_cv, for fitting popular machine learning methods in both a regression and a classification setting. Using the recent Stata/Python integration platform introduced in Stata 16, these commands provide hyperparameters’ optimal tuning via K-fold cross-validation using grid search. More specifically, they use the Python Scikit- learn application programming interface to carry out both cross-validation and outcome/label prediction.

Suggested Citation

  • Giovanni Cerulli, 2022. "Machine learning using Stata/Python," Stata Journal, StataCorp LP, vol. 22(4), pages 772-810, December.
    • Handle: RePEc:tsj:stataj:v:22:y:2022:i:4:p:772-810
    DOI: 10.1177/1536867X221140944
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    References listed on IDEAS

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    1. Cerulli, Giovanni, 2020. "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper 99111, University Library of Munich, Germany.
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    Cited by:

    1. Erokhin, Dmitry & Zagler, Martin, 2024. "Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms," Economic Modelling, Elsevier, vol. 139(C).
    2. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2023. "pystacked: Stacking generalization and machine learning in Stata," Stata Journal, StataCorp LP, vol. 23(4), pages 909-931, December.
    3. Lamperti, Fabio, 2024. "Unlocking machine learning for social sciences: The case for identifying Industry 4.0 adoption across business restructuring events," Technological Forecasting and Social Change, Elsevier, vol. 207(C).

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