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1.

You plan to use the tidymodels framework 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 is a risk of default that requires human consideration. What kind of model is required?

A binary classification model.

A multiclass classification model.

A linear regression model.

2.

You've trained a classification model specification in tidymodels. You want to use the model, logreg_cls_fit, to return labels for a new dataset called new_data. Which code should you use?

predict(logreg_cls_fit, new_data)

fit(logreg_cls_fit, new_data)

fit_resamples(logreg_cls_fit, new_data)

3.

You're training a binary classification model by using the tidymodels framework. 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 percent of the test cases.

81 percent of the cases predicted as positive by the model were actually positive.

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

You must answer all questions before checking your work.

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