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Which is the optimal point on the ROC curve?
The point on the ROC curve where a line with this slope S touches the curve is the optimal operating point, taking into account prevalence and the costs of the different decisions. Costs can be financial costs or health costs, but all 4 cost factors need to be expressed on a common scale.
Do you know the difference between ROC and AUC?
Unfortunately, many data scientists often just end up seeing the ROC curves and then quoting an AUC (short for the area under the ROC curve) value without really understanding what the AUC value means and how they can use them more effectively.
Which is the formula for calculating the AUC?
The formula for calculating the AUC for Example 1 (cell AP19) is =SUM (AP6:AP18). The calculated value of .845587 shows a pretty good fit. Observation: The Real Statistics Logistic Regression data analysis tool automatically creates the ROC curve as described above.
How is the ROC curve used in logistic regression?
The ROC curve is produced by calculating and plotting the true positive rate against the false positive rate for a single classifier at a variety of thresholds. For example, in logistic regression, the threshold would be the predicted probability of an observation belonging to the positive class.
What happens when you combine two predictors in a ROC curve?
If both predictors are above respective cutoffs then the result is positive. If one of them is below its cutoff (while the other is above) then the result is negative. If both are less than their cutoffs then the result is negative too. This is what “and” does.
Which is the correct ROC curve for perfect discrimination?
A test with perfect discrimination (no overlap in the two distributions) has a ROC curve that passes through the upper left corner (100% sensitivity, 100% specificity). Therefore the closer the ROC curve is to the upper left corner, the higher the overall accuracy of the test (Zweig & Campbell, 1993).
Is there a quantitative value for the AUC?
The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier. Is there any quantitative value for the AUC in order to segregate the quality of a classifier to be bad, fair, good, very good or excellent….or something like this?