Question 28
UnclassifiedNaive Bayes is called naive because it assumes:
Correct answer: B
Explanation
Naive Bayes is based on the simplifying assumption that the predictors are "conditionally independent given the class label." This means each feature contributes separately to the probability calculation once the class is known, which is why the model is called "naive."
Why each option is right or wrong
A. Features are linearly correlated
B. Features are conditionally independent given the class label
The model’s likelihood factorization uses the conditional-independence assumption: for a class c, P(x1, x2, ..., xn | c) = \u220f_i P(xi | c), which is the defining simplification in Naive Bayes. Without that assumption, the joint probability would require modeling all feature interactions, so the term "naive" refers to treating each predictor as independent once the class label is fixed.
C. The target is normally distributed
D. All classes are equally likely