Check your understanding of this module before moving on.
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Checkpoint quiz 3 questions
A quick check on this module's key ideas before you move on
Question 01
Why do ridge and lasso regression add a penalty term to the loss function?
B is correct. The penalty term discourages large coefficient values, which reduces variance and helps the model generalize instead of memorizing noise.
Question 02
What is the key practical difference between lasso and ridge regression?
A is correct. Lasso's L1 penalty can zero out coefficients entirely, effectively performing feature selection, while ridge's L2 penalty shrinks coefficients smoothly but keeps all of them in the model.
Question 03
What does Elastic Net combine?
B is correct. Elastic Net blends the L1 and L2 penalties so it can perform feature selection like lasso while keeping the stability of ridge when features are correlated.