Overfitting
When a model memorizes training data and fails to generalize to new data.
Overfitting occurs when a model learns the noise and specific patterns of its training data so thoroughly that it loses the ability to generalize to unseen examples. It essentially memorizes rather than learns, producing excellent training performance but poor results on validation or production data. Practitioners combat it with regularization, dropout, early stopping, cross-validation, and more training data. The key exam distinction is from underfitting: an overfit model shows high training accuracy but low test accuracy (too complex), while an underfit model performs poorly on both (too simple). Adding model capacity typically worsens overfitting.
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