Explain the difference between discriminative and generative models.

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Discriminative and generative models are two fundamental approaches in machine learning, especially in classification tasks. They differ in what they model and how they make predictions.

Discriminative Models

Discriminative models learn the decision boundary between classes. They model the conditional probability $P(y | x)$, where:

• $x$ is the input data

• $y$ is the label

These models focus directly on predicting the output label given the input. They don’t try to understand how the data was generated—just how to separate or classify it.

Examples:

• Logistic Regression

• Support Vector Machines (SVM)

• Neural Networks

• Conditional Random Fields (CRFs)

Pros:

• Usually achieve higher accuracy on classification tasks

• Require fewer assumptions about the data

Cons:

• Can't generate new data

• Don’t model the underlying data distribution

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Generative Models

Generative models learn how the data is generated by modeling the joint probability $P(x, y)$. From this, they can derive $P(y | x)$ using Bayes’ theorem.

They model both the distribution of inputs and labels, which allows them to generate new examples similar to the training data.

Examples:

• Naive Bayes

• Hidden Markov Models (HMMs)

• Gaussian Mixture Models

• Generative Adversarial Networks (GANs)

• Variational Autoencoders (VAEs)

Pros:

• Can generate new, synthetic data

• Often better with small datasets or missing data

Cons:

• Typically more complex and computationally intensive

• May be less accurate in pure classification tasks

Summary:

• Discriminative: Focus on classification

• Generative: Understand and recreate data distributions

Both approaches are valuable, depending on the task.

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