People always said that naive Bayes is a linear model. I am not able to understand why, so can anybody explain?
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Perhaps you could give a reference to where it's called linear and also say why this does not make sense to you? – Wayne Jun 28 '13 at 14:43
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This question contains the proof for a Multinomial Naive Bayes model. – Bharat Khatri Feb 08 '15 at 08:19
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I don't see how Naive Bayes is a linear model. This gives one possible definition. In general, I would say a linear model would involve a linear combination of the parameters or of some transformation of the parameters.
Naive Bayes multiplies the probabilities from the different variables and they are also not really weighted in the standard sense. However, if you take the logarithm this will become addition, so it is possible that given some type of conditional probability distribution (CPD) you could get something that looks like a linear model. I don't think this is true for every CPD though (but I may be wrong).
Bharat Khatri
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A linear model would involve a linear combination of the parameters or some transformation
of a linear combination of the parameters. – Bharat Khatri Feb 08 '15 at 08:16 -
If the predicted distribution is a noncurved exponential family, the naive Bayes prediction is a linear combination of natural parameters of predictive distributions. Is this what you're referring to? – Neil G Jun 28 '13 at 19:22