Overfitting

An overfitted model treats randomness and noise in the training data as patterns. It shines on familiar examples but fails in real use.

Remedies include more and more varied data, simpler models, regularisation and consistent testing with data the model never saw during training. The opposite is underfitting: the model is too simple to capture the patterns at all.