# ✅ Quiz M4.04b¶

Question

Regularization refers to:

• a) constraining the intercept of the linear model to be zero

• b) shrinking the weights of the linear model towards zero

• c) using a subset of the available data when fitting a linear model

Question

A ridge model is:

• a) the same as linear regression with penalized weights

• b) the same as logistic regression with penalized weights

• c) a linear model

• d) a non linear model

Question

Assume that a data scientist has prepared a train/test split and plans to use the test for the final evaluation of a Ridge model. The parameter alpha of the Ridge model:

• a) is internally tuned when calling fit on the train set

• b) should be tuned by running cross-validation on a train set

• c) should be tuned by running cross-validation on a test set

• d) must be a positive number

Question

Scaling the data before fitting a model:

• a) is necessary when using a regularized model

• b) is always necessary

• c) may speed-up fitting

• d) has no impact on the regularization parameter

Question

The effect of an l2-regularization (as done in ridge) is to:

• a) shrink all weights towards zero

• b) make all weights equal

• c) set a subset of the weights to exactly zero

• d) constrain all the weights to be positive