Overfitting occurs in machine learning and data science where a model is trained to such an extent that it begins to memorize the training data, rather than generalizing it. This can lead to poor performance on unseen data, as the model is unable to accurately predict outcomes for data points it has not seen before. Overfitting is caused by a model having too many parameters relative to the amount of training data, or by having too complex of a model for the data. To prevent overfitting, data scientists use techniques such as regularization, cross-validation, and early stopping.
