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Core
Generative vs Discriminative Models (Naive Bayes)
A discriminative model learns P(y|x) directly, the decision boundary. A generative model learns the joint P(x,y), so it models how the data is produced and derives the label via Bayes. Naive Bayes is the canonical generative classifier and leans on a strong conditional-independence assumption. Applied-AI interviews probe this to check whether you know that generative wins with little data or missing features while discriminative wins on raw accuracy once data is plentiful.
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