Question

1. Explain likelihood and marginal likelihood in context of naivebayes algorithm?

Answer 1

Answer:

Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence. ... c) is the likelihood which is the probability of predictor given class.

In statistics, a marginal likelihood function, or integrated likelihood, is a likelihood function in which some parameter variables have been marginalized. In the context of Bayesian statistics, it may also be referred to as the evidence or model evidence.

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