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Answer: The AUC shows pictorially how effective the model has been in separating the data points into clusters, with a higher AUC implying a better model fit, but the AUC cannot be used to compare between models.
## Explanation Let's analyze each statement: - **A**: Correct - The ROC curve plots True Positive Rate (TPR) on the y-axis against False Positive Rate (FPR) on the x-axis, and different points on the curve correspond to different classification thresholds. - **B**: **FALSE** - While the first part is correct (AUC shows model effectiveness and higher AUC implies better fit), the statement that "AUC cannot be used to compare between models" is incorrect. AUC is commonly used to compare different models' performance. - **C**: Correct - ROC and AUC are widely used in credit scoring and loan approval models to compare different classification models. - **D**: Correct - AUC ranges from 0 to 1, where 1 represents perfect classification and 0.5 represents random guessing. An AUC of 0.1 would indicate worse than random performance. Therefore, statement B is false because AUC can indeed be used to compare between models.
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A
The ROC curve plots the true positive rate on the x-axis against the false positive rate on the y-axis and the points on the curve emerge from varying the decision threshold.
B
The AUC shows pictorially how effective the model has been in separating the data points into clusters, with a higher AUC implying a better model fit, but the AUC cannot be used to compare between models.
C
One possible application of the ROC and AUC would be in the context of comparing models to determine whether a loan application should be rejected or accepted.
D
An AUC of 1 would indicate a perfect fit, whereas a value of 0.1 would correspond with an entirely random set of predictions and therefore a model with no predictive ability.
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