Overfitting

When a model memorises its training data instead of learning the pattern, and fails on anything new.

An overfitted model has learned the noise as well as the signal. It scores brilliantly on data it has seen and poorly on data it has not. The usual fixes are more data, less model capacity, regularisation, and stopping training early when validation loss turns upward.

In practice: 99% on training data, 62% in production — a textbook case.