βœ… 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 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