Overfitting occurs when a model learns the training data too well, including noise and outliers, resulting in poor generalization to new data. Signs include high training accuracy but low test accuracy. Regularization and cross-validation help prevent it.
Interactive lesson with visualizations and practice problems
Part of the Regularization lesson in Mathematics Foundations