Interpretable boosted naïve Bayes classification
Greg K. Ridgeway, David Madigan, Thomas S. Richardson, John W. O’Kane · 1998
Voting methods such as boosting and bagging provide substantial improvements in classification performance in many problem domains. However, the resulting predictions can prove inscrutable to end-users. This is especially problematic in domains such as medicine, where end-user acceptance often depends on the ability of a classifier to explain its reasoning. Here we propose a variant of the boosted naïve Bayes classifier that facilitates explanations while retaining predictive performance. Introduction Efforts to develop classifiers with strong discrimination power using voting methods have marginalized the importance of comprehensibility. Bauer and Kohavi [1998] state that "for learning tasks where comprehensibility is not crucial, voting methods are extremely useful." However, as many authors have pointed out, problem domains, such as credit approval and medical diagnosis, do require interpretable as well as accurate classification methods. For instance, Swartout [1983] commented tha...