Answer / Seema Pal

Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function during training. Regularization methods encourage simpler models that generalize better to unseen data. Examples of regularization techniques include L1 and L2 regularization (Ridge regression and Lasso), dropout, and early stopping.

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