Glossary

Supervised learning

A machine learning approach where a model learns from examples that already include the correct answer, to predict that answer on new data.

Supervised learning is the branch of machine learning where a model learns from labeled examples: input data paired with the known correct output, such as historical transactions labeled "fraud" or "not fraud." The model's job during training is to learn a general mapping from inputs to outputs that also holds for new, unlabeled cases.

It splits into two main problem types: classification, predicting a discrete category, and regression, predicting a continuous number, such as a price or a quantity. This differs from unsupervised learning, where the training data has no correct-answer labels and the model instead looks for structure on its own, and from reinforcement learning, where a model learns through trial and error and delayed reward rather than from a fixed labeled dataset.

Most production machine learning in analytics, fraud detection, demand forecasting, churn prediction, credit scoring, is supervised learning, because it maps naturally onto business problems that already have a historical record of outcomes to learn from. Its main dependency is the labeled data itself: labels are often expensive or slow to collect, can be inconsistent across labelers, and the resulting model can only be as good, and as unbiased, as the labels and examples it was trained on, a common source of overfitting to a specific dataset's quirks.

Last reviewed September 22, 2026

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Related terms

Unsupervised learning A machine learning approach that finds structure or patterns in data that has no labeled correct answer to learn from. Classification A supervised learning task that assigns each input to one of a fixed set of discrete categories or labels. Training data The dataset with known outcomes that a machine learning model learns patterns from before being used to predict new cases. Overfitting When a model learns the noise and specific quirks of its training data so closely that it performs poorly on new data. Reinforcement learning A learning approach where an agent learns a strategy by taking actions in an environment and receiving rewards or penalties over time.

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