Have you ever memorized test answers but failed the real exam? That is overfitting. In machine learning, a model learns training data too perfectly, capturing noise and outliers.
Instead of finding general patterns, it memorizes specifics. Consequently, it performs exceptionally well on training data but fails miserably on new, unseen data. To fix this common error, we use techniques like regularization, cross-validation, or gathering more diverse data. The ultimate goal is robust generalization, ensuring the model succeeds everywhere.