ROC Curve

Higher the AUC(Area Under The Curve), better the model is at predicting True postive. AUC represents degree or measure of separability

$TPR/Recall/Sensitivity = \frac{TP}{TP + FN}$

$Specificity=\frac{TP}{TP+FN}$

$FPR = 1- Specificity = \frac{FP}{TN +FP}$

The process to draw the ROC(Receiver Operating Characteristic curve) is: We select Threshold from 1 to 0, caculate the TPR and FRP to draw ROC.

123

The closer an ROC curve is to the upper left corner, the more efficient is the test.

Test A is better than Test B.

In multi-class model, we can plot N number of AUC ROC Curves for N number classes using One vs ALL methodology. So for Example, If you have three classes named X, Y and Z, you will have one ROC for X classified against Y and Z, another ROC for Y classified against X and Z, and a third one of Z classified against Y and X.