KFOLDCLASS: Stata module for generating classification statistics of k-fold cross-validation for binary outcomes

Ariel Linden

Statistical Software Components from Boston College Department of Economics

Abstract: kfoldclass performs k-fold cross-validation for regression and machine learning models with a binary outcome and then produces classification measures to assist in determining the error rate (or conversely, the accuracy) of a prediction (classification) model. In k-fold cross-validation, each of the k hold-out groups in turn is left out, and the logit, probit, randomforest, svmachines, or boosted regression model is estimated for all remaining (k-1) groups. The predicted value is then calculated for the one hold-out group, and the accuracy is determined as success or failure in predicting the outcome for that group. The results of all k predictions are used to calculate the final error estimates (accuracy) displayed in the classification table and ROC curves generated by kfoldclass.

Language: Stata
Requires: Stata version 11
Keywords: classification; cross validation; data mining; machine learning (search for similar items in EconPapers)
Date: 2017-10-13, Revised 2020-11-05
Note: This module should be installed from within Stata by typing "ssc install kfoldclass". 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/k/kfoldclass.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/k/kfoldclass.sthlp help file (text/plain)

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