ROC stands for 'Receiver Operating Characteristic'.It is a technique to visualize the performance of a binary classifier.
AUC, Area under curve will summarize the performance of a classifier in a single number.
ROC curve is a plot between True positive rate vs False positive rate at different threshold values.
True positive rate = no of true +ve vales predicted by classifier/ total no of +ve values
False positive rate= no of false +ve values predicted by classifier/ Total no of +ve values
Below shows a roc curve that represents ideal classifier
AUC, Area under curve will summarize the performance of a classifier in a single number.
ROC curve is a plot between True positive rate vs False positive rate at different threshold values.
True positive rate = no of true +ve vales predicted by classifier/ total no of +ve values
False positive rate= no of false +ve values predicted by classifier/ Total no of +ve values
Below shows a roc curve that represents ideal classifier
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