Supervised Learning: Components & Representation

Supervised learning fits a mapping from input features to known target values using labeled examples.

The algorithm adjusts model parameters to reduce a defined training objective.

Held-out metrics, cross-validation, learning curves, residuals, calibration plots, subgroup performance, and comparison with simple baselines reveal whether a model generalizes.