A generative model learns the different categories of data. On the other hand, a discriminative model will only learn the distinctions between different categories of data. Discriminative models perform much better than the generative models when it comes to classification tasks.

Generative and discriminative models are two fundamental approaches in machine learning that serve different purposes and have distinct characteristics:

Generative Models:
- Generative models aim to learn the joint probability distribution $P (X, Y)$ of the input features $X$ and the labels $Y$ .
- Once trained, a generative model can generate new samples that are similar to the training data by sampling from the learned distribution.
- Examples of generative models include Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), and Generative Adversarial Networks (GANs).
Discriminative Models:
- Discriminative models, on the other hand, focus solely on learning the conditional probability distribution $P (Y ∣ X)$ of the labels $Y$ given the input features $X$ .
- Discriminative models are particularly useful for classification tasks where the goal is to directly predict the label given the input.
- Examples of discriminative models include logistic regression, support vector machines (SVM), and neural networks (in their classification setups).

Key Differences:

Goal: Generative models aim to understand how the data is generated and can generate new samples, while discriminative models focus on classifying input data into predefined categories.
Training: Generative models typically involve learning a more complex joint distribution $P (X, Y)$ , whereas discriminative models learn a simpler conditional distribution $P (Y ∣ X)$ .
Use Cases: Generative models are useful when you need to generate new data samples, perform data augmentation, or model uncertainty. Discriminative models are commonly used when the focus is on classification or regression tasks.
Complexity: Discriminative models often have simpler structures and require fewer assumptions about the underlying data distribution compared to generative models.

In an interview, it’s important to articulate these differences clearly, providing examples and illustrating how each type of model is used in different contexts.