Which metric summarizes the ROC curve into a single number that indicates separation quality?

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Multiple Choice

Which metric summarizes the ROC curve into a single number that indicates separation quality?

Discrimination quality in a ROC curve is captured by a single number called the area under the ROC curve, usually abbreviated as AUC. The ROC curve shows how the true positive rate grows as you trade off false positives by varying the threshold. By computing the area under that curve, you get a summary of how well the model separates positives from negatives across all possible thresholds. A higher AUC means stronger separation, with values between 0.5 (no better than random) and 1.0 (perfect separation). This threshold-independent measure makes it ideal for comparing models. The option that includes AUC is the correct one. The other terms, like cleaning or tokenization, are preprocessing steps and don’t quantify separation quality.

Discrimination quality in a ROC curve is captured by a single number called the area under the ROC curve, usually abbreviated as AUC. The ROC curve shows how the true positive rate grows as you trade off false positives by varying the threshold. By computing the area under that curve, you get a summary of how well the model separates positives from negatives across all possible thresholds. A higher AUC means stronger separation, with values between 0.5 (no better than random) and 1.0 (perfect separation). This threshold-independent measure makes it ideal for comparing models. The option that includes AUC is the correct one. The other terms, like cleaning or tokenization, are preprocessing steps and don’t quantify separation quality.

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