Answer: (ii) Features are independent given the class

Explanation:

Naive Bayes uses: $P(C_k|x) \propto P(C_k)\prod_{i=1}^n P(x_i|C_k)$

Naive assumption: $x_i \perp x_j | C$, i.e., features conditionally independent given class label. This drastically reduces parameters from $O(2^n)$ to $O(n)$.

• (i) dependent — opposite of assumption
• (iii) class probabilities equal ($P(C_k)=1/K$) — not assumed; priors estimated from data $P(C_k)=N_k/N$
• (iv) no prior — false; needs $P(C_k)$ (estimated or uniform)

Therefore (ii) is correct. The independence is "naive" because in reality features often correlate, but model still works well in practice.