EMC Proven Professional – Data Science and Big Data Analytics — Question 19

When would you prefer a Naive Bayes model to a logistic regression model for classification?

Answer options

• A. When you are using several categorical input variables with over 1000 possible values each.
• B. When you need to estimate the probability of an outcome, not just which class it is in.
• C. When all the input variables are numerical.
• D. When some of the input variables might be correlated.

Correct answer: A

Explanation

The correct answer is A because Naive Bayes is particularly effective with high-dimensional categorical data, where features have many distinct values. Logistic regression may struggle with such data due to its assumptions about the distribution of the input features. Options B, C, and D do not accurately reflect the strengths and typical use cases of Naive Bayes compared to logistic regression.