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

Author

Listed:
  • Ariel Linden

    (Linden Consulting Group, LLC)

Programming Language

Stata

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.

Suggested Citation

  • Ariel Linden, 2025. "LOGLOSS: Stata module for computing the log loss metric for binary outcome models," Statistical Software Components S459412, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:s459412
    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.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/l/logloss.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/l/logloss.sthlp
    File Function: help file
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