Supervised Learning
Supervised learning trains a model on examples that already carry the correct answer, then uses the patterns it finds to predict that answer for new, unseen cases — the approach behind spam filters, fraud alerts, and price predictions.
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Intro
Supervised Learning
Supervised learning trains a model on examples that already have a known, correct answer attached. Each training example pairs an input (its features) with a target (the answer). The model learns a function that maps one to the other, then applies that function to inputs it has never seen.
That known-answer requirement is what separates supervised learning from unsupervised learning. A clustering algorithm has to guess at structure with no ground truth to check against. A supervised model gets graded against the right answer during training, which is exactly why it can be scored so precisely once it's built.
Classification and regression
Supervised learning splits into two problem shapes, based on what the target looks like.
Classification predicts a category: spam or not spam, which of ten digits a handwritten image shows, whether a transaction is fraudulent. The target is discrete.
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