C_ML_STATA_CV: Stata module to implement machine learning classification in Stata

Giovanni Cerulli

Statistical Software Components from Boston College Department of Economics

Abstract: c_ml_stata_cv is a command for implementing machine learning classification algorithms in Stata 16. It uses the Stata/Python integration (sfi) capability of Stata 16 and allows to implement the following classification algorithms: tree, boosting, random forest, regularized multinomial, neural network, naive Bayes, nearest neighbor, support vector machine. It provides hyper-parameters' optimal tuning via K-fold cross-validation using greed search. For each observation (or instance), this command generates both predicted class probabilities and predicted labels using the Bayes classification rule. This command makes use of the Python Scikit-learn API to carry out both cross-validation and prediction.

Language: Stata
Requires: Stata version 16.1
Keywords: machine learning; classification; boosting; random forest; neural network; tree; naive Bayes; nearest neighbor; support vector machine (search for similar items in EconPapers)
Date: 2022-03-22, Revised 2022-11-16
Note: This module should be installed from within Stata by typing "ssc install c_ml_stata_cv". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/c/c_ml_stata_cv.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_ml_stata_default.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_ml_stata_cv.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/p/pylearn.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/r/r_ml_stata_cv.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/g/get_train_test.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/g/get_train_test.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_boost.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_naivebayes.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_nearestneighbor.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_neuralnet.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_randomforest.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_regularizedmultinomial.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_multinomial.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_svm.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/c_tree.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/e/exampleFile-URL ... code/_/c_ml_stata.do sample file (text/plain)
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http://fmwww.bc.edu/repec/bocode/d/data_new_x.dta sample data file (application/x-stata)
http://fmwww.bc.edu/repec/bocode/d/data_new_y.dta sample data file (application/x-stata)

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