Explain ROC curve.

894    Asked by SanjanaShah in Data Science , Asked on Nov 30, 2019
Answered by Sanjana Shah

ROC or Receiver Operating Characteristic curve is a graphical representation that gives an idea between True Positive rate against False Positive Rate at various threshold values.

It gives an idea to set an optimum threshold value which will decide the probability assign a class. A low threshold value we will put most of the predicted observations under the positive category, even when some of them should be placed under the negative category. On the other hand, keeping the threshold at a very high level penalizes the positive category, but the negative category will improve.

For such case an optimum threshold value can give a better accuracy which can be found on ROC curve

ROC curve will look as follows:


 To gain more information on sensitivity and specificity, we need to go through the following formula.




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