Overfitting happens when a machine learning model learns its training data too closely, and includes the noise and irrelevant patterns within it instead of only paying attention to the underlying trends that actually matter. The result is a model that performs well on the data it was trained on but fails to make accurate predictions on new, unseen data.
One way to detect overfitting is to compare performance on training data against a separate test set. If the model scores well on training data but poorly on the test set, overfitting is likely the cause.
Overfitting typically occurs when a model is too complex for the amount of data available, or when training runs for too long. The opposite problem, underfitting, happens when the model is too simple or undertrained to capture meaningful patterns at all. Both produce poor results on new data, but for opposite reasons. The goal of model training is to find the right balance that enables the model to be flexible enough to learn real patterns but not so flexible that it memorizes the training data.
These are some techniques that prevent overfitting.
- Early stopping: Halting training before the model starts learning noise.
- Regularization: Penalizing overly complex parameter values to reduce variance.
- Data augmentation: Expanding the training set to give the model more patterns to learn from.
- Feature selection: Removing irrelevant or redundant inputs that add noise without improving predictions.
- Ensemble methods: Combining multiple models to produce more stable, generalized predictions
Recent research has shown that the relationship between complexity and performance is not always straightforward. In neural networks, pushing a model well beyond the point of memorizing its training data can sometimes cause performance to recover and even improve rather than continue to degrade. This pattern is known as double descent.
Related to overfitting is underfitting, which occurs when a machine learning model is too simple to learn the underlying patterns in its training data.