Neural Networks with Excel
Andrew Y. Ng and Michael I. Jordan had a classic paper on the comparison between logistic regression and naive Bayes. The main contribution of the paper is a theoretical analysis of how logistic regression and naive Bayes might perform and an experimental comparison to support this idea.
Several important points made by the paper. The first is that:
- The asymptotic error made by logistic regression is no more than that made by naive Bayes (Proposition 1 in the paper).
This conclusion provides a basis for what seems to be the widely held belief that discriminative classifiers are better than generative ones. The main conclusions of the paper is about the sample complexity of both classifiers. Sample complexity is the number of examples needed to approach the asymptotic error. For logistic regression, such sample complexity is:
m = \Omega(n)
\]which means that the sample complexity is linear in \( n \) (Proposition 2 in the paper). For naive Bayes, we have:
m = O(\log n)
\]which means that the sample complexity is logarithmic in \( n \) (Lemma 3 and Corollary 6 in the paper). All these conclusions imply that even though naive Bayes converges to a higher asymptotic error compared to logistic regression, it may also approach it significantly faster — after \( O(\log n) \), rather than \( O(n) \), training examples.
Source: Hong, LiangJie
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