machinelearning:: #regularization aims to improve generalizability and reduce overfitting by adjusting the loss term to discourage certain behaviors. The exact method can differ: L1 regularization punishes weights with high absolute values. L2 regularization adds the squared magnitude of the weights to the cost function. In SVMs, the regularizing term determined how much slack we gave to misclassifications.