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


  • Ariel Linden

    () (Linden Consulting Group)


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.

Suggested Citation

  • Ariel Linden, 2017. "KFOLDCLASS: Stata module for generating classification statistics of k-fold cross-validation for binary outcomes," Statistical Software Components S458412, Boston College Department of Economics, revised 19 Sep 2018.
  • Handle: RePEc:boc:bocode:s458412
    Note: This module should be installed from within Stata by typing "ssc install kfoldclass". Windows users should not attempt to download these files with a web browser.

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