Answer / Nandpal Tyagi

Regularization is a technique used in machine learning to prevent overfitting, where a model becomes too complex and performs poorly on unseen data. It works by adding a penalty term to the cost function (loss function) that encourages simpler models by penalizing large coefficients or weights. Common regularization techniques include L1 (Lasso) and L2 (Ridge) regularization, which differ in the type of penalty term applied.

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