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Aim

Theory

Pretest

Procedure

Simulation

Posttest

References

Feedback

Comparison of Linear, Lasso, and Ridge Regression

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What is the primary aim of using Lasso and Ridge regression in the experiment?

a: To improve prediction accuracy and model interpretability Explanation

Explanation

b: To increase the complexity of the model Explanation

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c: To eliminate the need for feature selection Explanation

Explanation

d: To decrease the training time Explanation

Explanation

Which regularization technique sets some coefficients to zero, effectively performing feature selection?

a: Ridge Regression Explanation

Explanation

b: Linear Regression Explanation

Explanation

c: Lasso Regression Explanation

Explanation

d: None of the above Explanation

Explanation

How does Ridge regression improve model stability?

a: By setting coefficients to zero Explanation

Explanation

b: By adding L2 regularization to discourage large coefficients Explanation

Explanation

c: By increasing the model’s complexity Explanation

Explanation

d: By removing irrelevant features automatically Explanation

Explanation

What is the primary evaluation metric used to compare the performance of the models?

a: Root Mean Squared Error (RMSE) Explanation

Explanation

b: R-squared (R²) Explanation

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c: Mean Squared Error (MSE) Explanation

Explanation

d: All of the above Explanation

Explanation

Which type of regularization does Ridge regression apply?

a: L1 norm Explanation

Explanation

b: L2 norm Explanation

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c: Combination of L1 and L2 Explanation

Explanation

d: No regularization Explanation

Explanation

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