Answer / Suman Nagar

"L1 Regularization: It adds a penalty proportional to the absolute value of coefficients in the model. This can help minimize overfitting by promoting simpler models with fewer features, but it may lead to underfitting if too much simplification occurs.nL2 Regularization: It adds a penalty proportional to the square of coefficients in the model. Unlike L1, it promotes models that are not overly complex but still well-fit the data without excessive noise."

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