# β Quiz M4.03#

Question

Which of the following estimators can solve linear regression problems?

• a) sklearn.linear_model.LinearRegression

• b) sklearn.linear_model.LogisticRegression

• c) sklearn.linear_model.Ridge

Question

Regularization allows:

• a) to create a model robust to outliers (samples that differ widely from other observations)

• b) to reduce overfitting by forcing the weights to stay close to zero

• c) to reduce underfitting by making the problem linearly separable

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 often useful for regularized linear models

• b) is always necessary for regularized linear models

• c) may speed-up fitting

• d) has no impact on the optimal choice of the value of a regularization parameter

Question

The effect of increasing the regularization strength in a ridge model 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

Question

By default, a LogisticRegression in scikit-learn applies:

• a) no penalty

• b) a penalty that shrinks the magnitude of the weights towards zero (also called βl2 penaltyβ)

• c) a penalty that ensures all weights are equal

Question

The parameter C in a logistic regression is:

• a) similar to the parameter alpha in a ridge regressor

• b) similar to 1 / alpha where alpha is the parameter of a ridge regressor

• c) not controlling the regularization

In logistic regression, increasing the regularization strength (by decreasing the value of C) makes the model:
• b) more confident: the values returned by predict_proba are closer to 0 or 1