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

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Authors Janez Demsar, Alessio Benavoli, Giorgio Corani, Marco Zaffalon
Journal/Conference Name Journal of Machine Learning Research
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language Jupyter Notebook
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