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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.
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