Overfitting

Overfitting is a phenomenon in which a model learns the training data so literally, including its noise and random outliers, that it loses the ability to generalize correctly to new, previously unseen data. It manifests as excellent results on the training set but significantly poorer performance in real-world applications, and is one of the main risks in the development of any AI model.