What does the area under ROC curve AUC represent?
AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.
How do you find the area under the ROC curve?
If the ROC curve were a perfect step function, we could find the area under it by adding a set of vertical bars with widths equal to the spaces between points on the FPR axis, and heights equal to the step height on the TPR axis.
Which is better the AUC or the ROC curve?
The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. When AUC = 1, then the classifier is able to perfectly distinguish between all the Positive and the Negative class points correctly.
What happens when AUC is 0.5 or higher?
When AUC=0.5, then the classifier is not able to distinguish between Positive and Negative class points. Meaning either the classifier is predicting random class or constant class for all the data points. So, the higher the AUC value for a classifier, the better its ability to distinguish between positive and negative classes.
How to calculate the area under a ROC curve?
You randomly pick on from the disease group and one from the no-disease group and do the test on both. The patient with the more abnormal test result should be the one from the disease group. The area under the curve is the percentage of randomly drawn pairs for which this is true (that is,…
How are ROC and AUC used in data science?
Every data scientists/ data science aspirants would have come across the concepts of ROC (Receiver Operating Characteristics) curve and AUC (Area Under Curve) and its applicability in evaluating the model quality. There are numerous blogs and tutorials which explain about them in detail.