Prevent overfitting by adding penalties to the objective function. L2 Regularization (Ridge) $$ \min_w |Xw - y|^2 + \lambda|w|^2 $$ 1from sklearn.linear_model import Ridge 2 3model = Ridge(alpha=1.0) # alpha = lambda 4model.fit(X, y) L1 Regularization (Lasso) $$ \min_w |Xw - y|^2 + \lambda|w|_1 $$ Promotes sparsity …