LOOCLASS: Stata module for generating classification statistics of Leave-One-Out cross-validation for binary outcomes
Ariel Linden ()
Statistical Software Components from Boston College Department of Economics
looclass performs leave-one-out 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. Leave-one-out cross-validation is n-fold cross-validation, where n is the number of observations in the dataset. Each observation in turn is left out, and the given model is estimated for all remaining observations. The predicted value is then calculated for the one hold-out observation, and the accuracy is determined as success or failure in predicting the outcome for that observation. The results of all n predictions are used to calculate the final error estimates (accuracy) displayed in the classification table and ROC analyses generated by looclass.
Requires: Stata version 13
Keywords: leave-one-out cross validation; classification; data mining; machine learning (search for similar items in EconPapers)
Date: 2015-06-25, Revised 2020-11-05
Note: This module should be installed from within Stata by typing "ssc install looclass". 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/l/looclass.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/looclass.sthlp help file (text/plain)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocode:s458032
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