Regularization

Techniques to prevent overfitting.

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Regularization comprises methods that reduce overfitting by penalizing model complexity or augmenting training data.

• Examples: L1/L2 penalties, dropout, data augmentation.
• Goal: Achieve balance between accuracy and generalization.