How does regularization change the optimization objective?

Question

Anonymous · Accepted Answer

Regularization changes the optimization objective by adding a penalty term that
forces coefficients to stabilize, pushing them towards zero or depending on the
penalty term, exactly to 0.

Anonymous · Answer

Regularization adds penalty terms to the loss function, which is the original
objective function that the model tries to minimize. The penalty terms
correspond to the multiplication of model weights with penalty/cost values. This
forces the model to balance data accuracy with model simplicity.

How does regularization change the optimization objective?

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