# β Quiz M4.04bΒΆ

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 setb) should be tuned by running cross-validation on a

**train set**c) should be tuned by running cross-validation on a

**test set**d) can be chosen by hand a priori using expert knowledge of the problem at hand

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