Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
This addresses the problem of flawed statistical validation in machine learning research, advocating for a paradigm shift that could improve result reliability across the field.
The paper argues for replacing null hypothesis significance testing (NHST) with Bayesian analysis in machine learning to address statistical shortcomings, proposing more sound alternatives for comparing multiple classifiers.
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.