Question: In the context of the ROC curve for binary classifiers, what does the false positive rate (FPR) represent? The ratio of negative instances incorrectly classified

In the context of the ROC curve for binary classifiers, what does the false positive rate (FPR) represent? The ratio of negative instances incorrectly classified as positive. 5 The ratio of positive instances correctly classified. The precision of the classifier. The specificity of the classifier

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