LOGLOSS: Stata module for computing the log loss metric for binary outcome models

Ariel Linden ()
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Ariel Linden: Linden Consulting Group, LLC

Statistical Software Components from Boston College Department of Economics

Abstract: logloss computes the log loss metric to assess the accuracy of a binary prediction model. The log loss metric is considered to be more sensitive than brier in distinguishing between good and poor predictive models. The log loss ranges from 0 to infinity, where a lower score indicates better performance. A perfect model would have a log loss of 0, while a random model would have a log loss of around 0.693.

Language: Stata
Requires: Stata version 11
Keywords: log loss; classification accuracy (search for similar items in EconPapers)
Date: 2025-01-22
Note: This module should be installed from within Stata by typing "ssc install logloss". 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/logloss.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/logloss.sthlp help file (text/plain)

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