Regularization
Techniques that add constraints to model training to prevent overfitting and improve generalization.
In more detail
Regularization methods include L1/L2 penalties (weight decay), dropout, early stopping, and data augmentation. They reduce model complexity or constrain weights, forcing the model to learn simpler patterns that generalize better. Critical for improving test performance.
Examples
- L1 and L2 regularization
- Dropout layers
- Early stopping
- Data augmentation
Related terms
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