Question

What is the limitation of Bayesian classifier model? How does Naïve Bayes classifier model overcome the limitation? State the related assumption. 4.

Answer 1

Issue 2: Continuous variables

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When an attribute is continuous, computing the probabilities by the traditional method of frequency counts is not possible. In this case we would either need to convert the attribute to a discrete variable or use probability density functions to compute probability densities (not actual probabilities!). Most standard implementation automatically account for nominal and continuous attributes so the user does not need to worry about these transformations. However as a data scientist, it is important to be aware of the subtleties in the tool application.Issue 3: Attribute in

This is by far the most important weakness and something which requires a little bit of extra effort. In the calculation of o

dependenceutcome probabilites using the classical Bayes theorem, the implicit assumption is that all the attributes are mutually independent. This allows us to multiply the class conditional probabilities in order to compute the outcome probability.