2017
Benavoli, Alessio; Corani, Giorgio; Demsar, Janez; Zaffalon, Marco
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis Journal Article
In: Journal of Machine Learning Research, vol. 18, no. 77, pp. 1-36, 2017.
Abstract | Links | BibTeX | Tags: bayesian statistics, machine learning
@article{benavoli2016e,
title = {Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis},
author = {Benavoli, Alessio and Corani, Giorgio and Demsar, Janez and Zaffalon, Marco},
url = {http://jmlr.org/papers/v18/16-305.html},
year = {2017},
date = {2017-10-02},
journal = {Journal of Machine Learning Research},
volume = {18},
number = {77},
pages = {1-36},
abstract = {The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most radical cases even banned its use in publications. We should do the same: just as we have embraced the Bayesian paradigm in the development of new machine learning methods, so we should also use it in the analysis of our own results. We argue for abandonment of NHST by exposing its fallacies and, more importantly, offer better - more sound and useful - alternatives for it. },
keywords = {bayesian statistics, machine learning},
pubstate = {published},
tppubtype = {article}
}
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most radical cases even banned its use in publications. We should do the same: just as we have embraced the Bayesian paradigm in the development of new machine learning methods, so we should also use it in the analysis of our own results. We argue for abandonment of NHST by exposing its fallacies and, more importantly, offer better - more sound and useful - alternatives for it.