The GATE Grind

GATE 2026 DA – Question 37

Machine Learning · Supervised learning: regression · 2 marks · Multiple choice

Which of the following statements is true for Ridge Regression?

  1. The regularizer in the objective function of Ridge Regression is used to guard against scenarios where the model works well for the test data, but poorly for the training data.
  2. The regularizer of Ridge Regression uses $L_1$ norm.
  3. Ridge Regression aims to reduce the number of parameters that have negative values.
  4. The regularizer of Ridge Regression may increase the bias of the model, but it helps in reducing the variance in predictions.

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Correct answer: (D) The regularizer of Ridge Regression may increase the bias of the model, but it helps in reducing the variance in predictions.

Explanation

Ridge regression adds a penalty on the squared $L_2$ norm of the weights. It shrinks the weights, which adds a little bias but makes the model less sensitive to the training sample, so the variance falls. It guards against overfitting, which is the opposite of the case in A. The $L_1$ norm is used in lasso, and ridge does not care about the signs of the weights.