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Area under the ROC curve: Difference between revisions
Created page with "{{see also|Machine learning terms}} ==Introduction== The Receiver Operating Characteristic (ROC) curve is a widely-used visual representation of the performance of binary classifiers. It plots True Positive Rate (TPR) against False Positive Rate (FPR) over various threshold values for each classifier. The area under the ROC curve (AUC) serves as an aggregate metric that summarizes overall classifier performance across all possible threshold values. ==Methodology== Calcu..."
(Created page with "{{see also|Machine learning terms}} ==Introduction== The Receiver Operating Characteristic (ROC) curve is a widely-used visual representation of the performance of binary classifiers. It plots True Positive Rate (TPR) against False Positive Rate (FPR) over various threshold values for each classifier. The area under the ROC curve (AUC) serves as an aggregate metric that summarizes overall classifier performance across all possible threshold values. ==Methodology== Calcu...") |
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