Overfitting

Overfitting is a common problem in machine learning and statistical modeling where a model learns to memorize the training data excessively, capturing noise and irrelevant patterns instead of generalizing well to unseen data. It occurs when a model is too complex relative to the amount of training data available, leading to poor performance on new or unseen examples. Readers can explore techniques such as regularization, cross-validation, and early stopping to mitigate overfitting and improve the generalization performance of machine learning models.