Supervised Learning

Training on examples that come with the right answer attached, so the model learns to map inputs to known labels.

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Definition

In supervised learning every training example is a pair: an input and the correct output. The model learns the mapping between them, and is judged on how well it reproduces the mapping for inputs it has never seen.

Classification predicts a category, regression predicts a number, and both are supervised. The constraint is labels: they are expensive, often require expertise, and the model inherits whatever bias went into producing them.

How it relates to other terms

Contrast with Unsupervised Learning Finding structure in data that has no labels — clusters, groupings, or a compressed representation. Contrast with Reinforcement Learning Learning by acting in an environment and adjusting behaviour based on rewards, rather than from labelled examp... See also Overfitting When a model learns the training data so closely — including its noise — that it performs worse on anything ne...
Referenced by
Overfitting See also Unsupervised Learning Contrast with Reinforcement Learning Contrast with Transfer Learning See also

Related terms

Embedding

A list of numbers representing a piece of data, arranged so that similar things end up close together.

Intermediate Machine Learning

Neural Network

A model built from layers of simple weighted units, trained by adjusting those weights until the output matches the target.

Beginner Machine Learning

Overfitting

When a model learns the training data so closely — including its noise — that it performs worse on anything new.

Beginner Machine Learning

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