What’s the difference between a generative and discriminative model?

Generative and discriminative models are two different types of machine learning models used for various tasks, primarily in the field of supervised learning. They have different objectives and approaches:

1. Generative Models:

   • Objective: Generative models aim to model the underlying probability distribution of the data. In other words, they learn how data points are generated from an underlying model.
   • Use Cases: Generative models are often used for tasks like data generation, data completion, and unsupervised learning. They can generate new data points that are similar to the training data.
   • Examples: Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).
   • Training: Generative models are typically trained to maximize the likelihood of the training data. They learn to capture the joint distribution of features and labels (if available).

2. Discriminative Models:

   • Objective: Discriminative models aim to model the conditional probability of a label (or class) given the input data. In other words, they learn to discriminate between different classes or categories of data.
   • Use Cases: Discriminative models are commonly used for tasks like classification, regression, and structured prediction. They help make decisions or predictions based on input data.
   • Examples: Logistic Regression, Support Vector Machines (SVMs), Decision Trees, Random Forests, Neural Networks (when used for classification or regression), and Conditional Random Fields (CRFs).
   • Training: Discriminative models are typically trained to maximize the likelihood of the labels given the input data. They focus on modeling the conditional distribution of labels.