Answer: (ii) Assumes that all features are independent (given the class) — note option numbering duplicate, intended as (ii). Strictly Naive Bayes assumes conditional independence; (i) is not an assumption.

Clarification:

Naive Bayes classifier: $P(y|x_1..x_n) \propto P(y)\prod_i P(x_i|y)$

• Assumption: Features are conditionally independent given class $x_i \perp x_j | y$. That is (ii) essentially. This is strong/naive.
• It does NOT assume all features equally important (i) — importance is implicitly learned via $P(x_i|y)$ likelihoods; correlated important features still get weight. Equal importance is not required.
• The option list has typo: second (ii) should be (iii) Both (i) and (ii). Since (i) is false, Both is false.
• Therefore correct is only independence assumption → (ii). Some keys mark (ii) Both due to loose teaching, but precise answer is independence only.

If exam expects textbook shortcut "Naive Bayes assumes independence and equal importance", some mark (iii) Both — mention this nuance.