# Supervised learning

Supervised learning is a branch of machine learning and artificial intelligence. It involves training algorithms using labeled example data consisting of input-output pairs.

Key aspects:

- Algorithms learn a function that maps inputs to outputs.
- Input data is accompanied by corresponding targets representing desired outputs.
- Models are trained by measuring error between predictions and targets.
- Supervision provides feedback to optimize and tailor the model.
- Aims to approximate the mapping function to make accurate predictions for new unlabeled inputs.

Applications include:

- Classification algorithms categorize data into predefined classes.
- Regression algorithms predict continuous-valued outputs.

Popular techniques include linear regression, random forests, neural networks, support vector machines.

Supervised learning excels at pattern recognition in problems with historical training data. It is widely used for predictive modeling and analysis.

See also: