Interpretable Boosted Naive Bayes Classification

Greg Ridgeway, David Madigan, Thomas Richardson, and John O'Kane

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 naive Bayes classifier that facilitates explanations while retaining predictive performance.

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