Definition
Supervised Learning
Supervised learning trains models on labeled examples so they can predict labels, scores or values for new data.
Short definition
Supervised learning is a machine learning setup where each training example includes both input data and the correct answer. The model learns to map inputs to outputs and is later tested on new cases.
How it works
The training data might pair customer records with churn labels, images with object labels or transactions with fraud outcomes. The model makes predictions, compares them to labels and updates itself to reduce mistakes.
Example
A bank can train a supervised model on past transactions marked as fraudulent or legitimate. When a new transaction arrives, the model estimates the probability that it is suspicious.
Why it matters
Supervised learning is practical when good labels exist. Its quality depends heavily on label accuracy, dataset coverage and evaluation. If historical labels contain bias or errors, the model can learn and repeat those problems.