Module assessment

3 minutes

Answer the following questions to check your understanding.

You plan to use Scikit-Learn to train a model that predicts credit default risk. The model must predict a value of 0 for loan applications that should be automatically approved, and 1 for applications where there's a risk of default that requires human consideration. What kind of model is required?

A binary classification model

A multi-class classification model

A linear regression model

You have trained a classification model using the Scikit-Learn LogisticRegression class. You want to use the model to return labels for new data in the array x_new. Which code should you use?

model.predict(x_new)

model.fit(x_new)

model.score(x_new, y_new)

You train a binary classification model using Scikit-Learn. When you evaluate it with test data, you determine that the model achieves an overall Recall metric of 0.81. What does this metric indicate?

The model correctly predicted 81% of the test cases

81% of the cases predicted as positive by the model were positive.

The model correctly identified 81% of positive cases as positive.

You must answer all questions before checking your work.

Module assessment

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