The Learnability of Naive Bayes

Authors:
Huajie Zhang;Charles X. Ling;Zhiduo Zhao
Affiliations:
-;-;-
Venue:
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Year:
2000

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Abstract

Naive Bayes is an efficient and effective learning algorithm, but previous results show that its representation ability is severely limited since it can only represent certain linearly separable functions in the binary domain. We give necessary and sufficient conditions on linearly separable functions in the binary domain to be learnable by Naive Bayes under uniform representation. We then show that the learnability (and error rates) of Naive Bayes can be affected dramatically by sampling distributions. Our results help us to gain a much deeper understanding of this seemingly simple, yet powerful learning algorithm.