Understanding ROC Curves in Binary Classification

ROC stands for Receiver Operating Characteristic. It is a graphical representation of the performance of a binary classifier, specifically the trade-off between the true positive rate (Sensitivity) and the false positive rate (1 - Specificity). The ROC curve plots the true positive rate against the false positive rate at different thresholds.

The ROC curve can be used to compare the performance of different classifiers, as well as to evaluate the performance of a single classifier over a range of operating points. It is a useful tool for evaluating the performance of machine learning models in binary classification tasks.

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