![SOLVED: One measure of the overall success of a test is the area under the ROC curve (AUC), defined as AUC = Pr[TP]ov) dv where Pr[TP] is function of v = Pr[FP]: SOLVED: One measure of the overall success of a test is the area under the ROC curve (AUC), defined as AUC = Pr[TP]ov) dv where Pr[TP] is function of v = Pr[FP]:](https://cdn.numerade.com/ask_images/8f0e83d33a8d4a5d8f3ba7d785416bf3.jpg)
SOLVED: One measure of the overall success of a test is the area under the ROC curve (AUC), defined as AUC = Pr[TP]ov) dv where Pr[TP] is function of v = Pr[FP]:
![How to Use ROC Curves and Precision-Recall Curves for Classification in Python - MachineLearningMastery.com How to Use ROC Curves and Precision-Recall Curves for Classification in Python - MachineLearningMastery.com](https://machinelearningmastery.com/wp-content/uploads/2018/08/ROC-Curve-Plot-for-a-No-Skill-Classifier-and-a-Logistic-Regression-Model.png)
How to Use ROC Curves and Precision-Recall Curves for Classification in Python - MachineLearningMastery.com
![An example of ROC curves with good (AUC = 0.9) and satisfactory (AUC =... | Download Scientific Diagram An example of ROC curves with good (AUC = 0.9) and satisfactory (AUC =... | Download Scientific Diagram](https://www.researchgate.net/publication/276079439/figure/fig2/AS:614187332034565@1523445079168/An-example-of-ROC-curves-with-good-AUC-09-and-satisfactory-AUC-065-parameters.png)