Machine learning using Stata/Python

Giovanni Cerulli

Stata Journal, 2022, vol. 22, issue 4, 772-810

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.

Keywords: r_ml_stata_cv; c_ml_stata_cv; get_test_train; machine learning; Python; optimal tuning (search for similar items in EconPapers)
Date: 2022
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-4/pr0076/
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=pr0076 link to article purchase

Related works:
Working Paper: Machine learning using Stata/Python (2022)
Working Paper: Machine learning using Stata/Python (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:22:y:2022:i:4:p:772-810

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

DOI: 10.1177/1536867X221140944

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().