Overfitting occurs when a model learns the training data so closely, including its noise and random quirks, that it fails to generalize to new, unseen data it wasn't trained on. An overfit model can achieve near-perfect accuracy on the exact examples it was trained with, while performing noticeably worse on new examples drawn from the same underlying problem.
This typically happens when a model is too complex relative to the amount or diversity of training data available, effectively memorizing specific examples rather than learning the broader underlying patterns that would actually transfer to new situations. Common techniques for preventing or reducing overfitting include using more diverse training data, simplifying the model architecture, and regularization methods that specifically penalize the model for relying too heavily on any single pattern in the training set.
