Overfitting is a common problem in machine learning where a model learns the training data too well, capturing noise and random fluctuations rather than the underlying patterns. An overfitted model performs exceptionally on training data but fails to generalize to new, unseen data. This results in high accuracy during training but poor predictive performance in real-world applications.
Several factors can contribute to overfitting, including using an overly complex model, training for too many epochs, or having insufficient training data. When a model has too many parameters relative to the amount of training data, it essentially memorizes the data rather than learning generalizable features. This is particularly common with deep neural networks that have millions of parameters.
To combat overfitting, practitioners employ various regularization techniques such as L1/L2 regularization, dropout, early stopping, and data augmentation. Cross-validation is also used to assess how well a model generalizes by evaluating it on separate validation and test sets. Striking the right balance between underfitting (too simple) and overfitting (too complex) is a fundamental challenge in building effective machine learning systems.