✅ 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

Select all answers that apply

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

Select a single answer

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

Select all answers that apply

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

Select all answers that apply

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

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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

Select all answers that apply

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 “l2 penalty”)

  • c) a penalty that ensures all weights are equal

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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

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Question

In logistic regression, increasing the regularization strength (by decreasing the value of C) makes the model:

  • a) more likely to overfit to the training data

  • b) more confident: the values returned by predict_proba are closer to 0 or 1

  • c) less complex, potentially underfitting the training data

Select a single answer