Natural Language Processing
The area under the ROC curve (AUC) is a metric that evaluates the performance of a binary classification model by measuring the degree to which it can distinguish between positive and negative classes. The ROC curve itself is a graphical representation that plots the true positive rate against the false positive rate at various threshold settings. A higher AUC value indicates better model performance, with an AUC of 1 representing a perfect model and an AUC of 0.5 indicating no discriminative ability.
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