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Performance Comparison of Classifiers: Frequentist and Bayesian

  • Speaker: Dr Dell Zhang, Birkbeck, University of London.
  • Date: Tuesday, 8 November 2016 from 17:00 to 18:00
  • Location: Room 151

Suppose that you have developed a new classification algorithm and it achieves a higher performance score than the state of the art on a test dataset. How can you know whether the performance improvement is genuine but not because the new classifier happens to work better on that particular test dataset by luck? The standard method to address this problem is to carry out null hypothesis significance testing (NHST), e.g., using the t-test. In this talk, I would like to discuss the limitations of such a frequentist approach, and introduce our recently proposed Bayesian models which can supersede NHST.