ROC & AUC

ROC: Receiver Operating Characteristic
A ROC curve is a graphic plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.

 The ROC curve is created by plotting the true positive rate(TPR) against the false positive rate(FPR) at various threshold settings.

 The true positive rate is also know as sensitivity, recall or probability of detection in machine learning.

 Acc = (True Positive + True Negative)/Total population
F1 Score = 2*(Precision*Recall)/(Precision + Recall)

False positive: type I error
False negative: type II error

 AUC: Area Under the Curve
When using normalized units, the area under the curve is equally to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.

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