Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis.
JOURNAL OF MACHINE LEARNING RESEARCH(2017)
摘要
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.
更多查看译文
关键词
comparing classifiers,null hypothesis significance testing,pitfalls of p-values,Bayesian hypothesis tests,Bayesian correlated t-test,Bayesian hierarchical correlated t-test,Bayesian signed-rank test
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络