Answer: (iii) Naive Bayes

Naive Bayes is explicitly probabilistic: it applies Bayes' theorem, $P(C \mid X) = \dfrac{P(X \mid C)\,P(C)}{P(X)}$, assuming naively that features are conditionally independent given the class, and predicts the class with the maximum posterior probability. Its output can be read directly as class probabilities.

A decision tree chooses splits by information gain or Gini impurity — a deterministic, rule-based model — and an SVM finds a maximum-margin hyperplane by convex optimisation, not by a probability model. KNN does give a rough frequency estimate through neighbour voting, but it has no underlying probabilistic model; the unambiguously probabilistic classifier here is Naive Bayes.