GATE DA 2026 Set 1 — Question 37
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Machine Learning → Model Evaluation & Validation → Bias-Variance Trade-off
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Question
Which of the following statements is true for Ridge Regression?
Correct answer
(D) The regularizer of Ridge Regression may increase the bias of the model, but it helps in reducing the variance in predictions.
Solution
Ridge Regression adds an penalty term (regularizer) to the loss function. This regularization technique is used to prevent overfitting (where the model performs well on training data but poorly on test data), not the other way around as stated in option (A).
- Option (B) is incorrect because Ridge Regression uses the norm (squared magnitude of coefficients), while Lasso Regression uses the norm.
- Option (C) is incorrect because Ridge Regression shrinks coefficients towards zero but does not specifically target negative values or enforce sparsity.
- Option (D) is correct. By introducing a penalty term, Ridge Regression constrains the coefficients, which increases the bias of the model (it fits the training data less perfectly) but reduces the variance (it generalizes better to new data), thus optimizing the bias-variance tradeoff.
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