SOTAVerified

Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis

2016-06-14Code Available1· sign in to hype

Alessio Benavoli, Giorgio Corani, Janez Demsar, Marco Zaffalon

Code Available — Be the first to reproduce this paper.

Reproduce

Code

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.

Tasks

Reproductions