Overview
Overfitting happens when a model becomes too complex and starts 'memorizing' the training data, including its noise, rather than learning the underlying patterns.
Symptoms
High accuracy on training data but low accuracy on validation or real-world data.
Prevention
- Using more training data.
- Regularization: Techniques that penalize model complexity.
- Early Stopping: Stopping training before the model starts to overfit.