Answer: (iii) L2 norm

Explanation:

Ridge Regression (Tikhonov regularization) adds L2 penalty to OLS loss:

$J(w) = \sum_{i=1}^n (y_i - w^T x_i)^2 + \lambda ||w||_2^2$ where $||w||_2^2 = \sum_j w_j^2$, $\lambda >0$

• Shrinks coefficients toward zero, handles multicollinearity, reduces variance / overfitting.
• Closed form: $w_{ridge} = (X^T X + \lambda I)^{-1} X^T y$

Contrast:

• L1 norm (i) → Lasso regression, induces sparsity/feature selection.
• Entropy (ii) / Cross-entropy (iv) → used in logistic regression / decision trees, not ridge.

So ridge = L2.