Answer / Mahendra Pratap

A generative model learns to create probability distributions over data, whereas a discriminative model focuses on learning to classify instances based on their labels. Generative models are used when we want to understand the underlying structure of the data, like in image generation or speech synthesis. Discriminative models are commonly used for classification tasks like spam detection and sentiment analysis because they can directly map input data to output classes without understanding the data distribution.

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