Overfitting occurs when a machine learning model learns the training data too well, capturing noise and random fluctuations rather than the underlying pattern. As a result, the model performs exceptionally well on the training data but fails to generalize to new, unseen data. It's like memorizing the answers to a test instead of learning the subject.
A fundamental concept in statistics and machine learning related to the bias-variance tradeoff. The problem has been recognized since the early days of statistical modeling.
Overfitting is a common pitfall in training complex models like deep neural networks. Techniques to prevent it, such as regularization (L1/L2, Dropout), early stopping, and data augmentation, are standard practices in the field.