A Bayesian Metric for Evaluating Machine Learning Algorithms

摘要

How to assess the performance of machine learning algorithms is a problem of increasing interest and urgency as the data mining application of myriad algorithms grows. Rather than predictive accuracy, we propose the use of information-theoretic reward functions. The first such proposal was made by Kononenko and Bratko. Here we improve upon our alternative Bayesian metric, which provides a fair betting assessment of any machine learner. We include an empirical analysis of various Bayesian classification learners.