What is AUC Score?

In machine learning, the area under the curve (AUC) score is a measure of the performance of a binary classifier. AUC score is calculated by plotting the true positive rate (TPR) against the false positive rate (FPR) at different classification thresholds. The AUC score is the area under the ROC curve. 

A ROC curve is a graphical representation of the trade-off between TPR and FPR. The TPR is the proportion of positive instances that are correctly classified as positive, while the FPR is the proportion of negative instances that are incorrectly classified as positive.

AUC = ∫0^1 TPR(FPR) dFPR

The AUC score and prediction are related in the sense that a classifier with a higher AUC score is more likely to make accurate predictions.

The AUC score is a measure of the overall performance of a binary classifier. A higher AUC score indicates that the classifier is better at distinguishing between positive and negative instances.

An AUC score of 1.0 indicates a perfect classifier, while an AUC score of 0.5 indicates a random classifier. AUC scores are typically interpreted as follows:

0.90-1.00Excellent performance
0.70-0.90Good performance
0.50-0.70Fair performance
0.50Random performance
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