On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Andrew Ng, Michael Jordan


We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely(cid:173) held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of per(cid:173) formance as the training set size is increased, one in which each algorithm does better. This stems from the observation- which is borne out in repeated experiments- that while discriminative learning has lower asymptotic error, a generative classifier may also approach its (higher) asymptotic error much faster.