What are the differences between supervised, unsupervised, and generative learning?

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Supervised, unsupervised, and generative learning are three core paradigms in machine learning, each with distinct goals and data requirements.

Supervised learning relies on labeled datasets, where each input is paired with a correct output. The model learns to map inputs to outputs by minimizing prediction errors. It’s commonly used for tasks like classification (e.g., spam detection) and regression (e.g., price prediction). Key algorithms include linear regression, support vector machines, and neural networks.

Unsupervised learning deals with unlabeled data. The goal is to discover hidden patterns or structures without explicit output labels. It’s often used for clustering (e.g., customer segmentation) and dimensionality reduction (e.g., PCA). Common algorithms include k-means, hierarchical clustering, and autoencoders.

Generative learning focuses on modeling the joint probability distribution of inputs and outputs (P(x, y)) or just the input distribution (P(x)), allowing the generation of new, similar data. Unlike discriminative models (which model P(y|x)), generative models can create new samples. Examples include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Naive Bayes.

In summary:

• Supervised: Labeled data, predicts outcomes.

• Unsupervised: Unlabeled data, finds structure.

• Generative: Models data distribution, generates new data.

Each approach serves different tasks and often complements the others in modern machine learning systems.

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