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Explaining Naive Bayes Classifications

University of Alberta Library2003

Citations Over TimeTop 11% of 2003 papers

Russell Greiner, Brett Poulin, Paul Lu, Anvik, J., Zhonghua Lu, Cam Macdonell, David S. Wishart, Roman Eisner, Duane Szafron

Abstract

Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a novel graphical explanation facility for Naive Bayes classifiers that serves three purposes. First, it transparently explains the reasoning used by the classifier to foster user confidence in the prediction. Second, it enhances the user's understanding of the complex relationships between the features and the labels. Third, it can help the user to identify suspicious training data. We demonstrate these ideas in the context of our implemented web-based system, which uses examples from molecular biology.

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