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Machine learning using Stata/Python

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  • Giovanni Cerulli

    (IRCrES-CNR)

Abstract

We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular machine learning (ML) methods in both regression and classification settings. Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyperparameters' optimal tuning via K-fold cross-validation using greed search. More specifically, they make use of the Python Scikit-learn API to carry out both cross-validation and outcome/label prediction.

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  • Giovanni Cerulli, 2021. "Machine learning using Stata/Python," 2021 Stata Conference 25, Stata Users Group.
    • Handle: RePEc:boc:scon21:25
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    File URL: http://fmwww.bc.edu/repec/scon2021/US21_Cerulli.pdf
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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. 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.

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