Popular interview question: Difference between generative and discriminative models

You have heard of Generative Ai. Now what the hell are discriminative models?

Based on the data modeling approach, ML models can be classified into two categories:

• Generative

• Discriminative

We prepared the following visual which depicts how they differ.

Discriminative models:

• learn decision boundaries that separate different classes.

• maximize the conditional probability: P(Y|X) — Given X, maximize the probability of label Y.

• are specifically meant for classification tasks.

Generative models:

• maximize the joint probability: P(X, Y)

• learn the class-conditional distribution P(X|Y)

• are typically not preferred to solve downstream classification tasks.

Since generative models learn the underlying distribution, they can generate new samples. But this is not possible with discriminative models.

Furthermore, generative models possess discriminative properties, i.e., they can be used for classification tasks (if needed). But discriminative models do not possess generative properties.

Author

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