Overfitting

Overfitting happens when a machine learning model performs well on training data but poorly on unseen data, as it has essentially memorized the training examples rather than learning to generalize from them. This often occurs when a model is too complex relative to the amount of training data available. Techniques such as cross-validation, regularization, and pruning are used to combat overfitting and improve model generalization.