What is supervised learning?

Supervised learning is a type of ML where the model is trained on labeled data — each training example has an input and a known correct output (label). The model learns to map inputs to outputs by minimizing the error between its predictions and the true labels. Common algorithms include linear regression, decision trees, and neural networks. Examples: spam classification (input = email, label = spam/not spam) and house price prediction (input = features, label = price).