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How do you select threshold with ROC curve?
6 Answers
- Adjust some threshold value that control the number of examples labelled true or false.
- Generate many sets of annotated examples.
- Run the classifier on the sets of examples.
- Compute a (FPR, TPR) point for each of them.
- Draw the final ROC curve.
Where is threshold in ROC curve?
The threshold is then used to locate the true and false positive rates, then this point is drawn on the ROC Curve. We can see that the point for the optimal threshold is a large black dot and it appears to be closest to the top-left of the plot.
Is there a way to drop thresholds in ROC curve?
As HaohanWang mentioned, the parameter ‘ drop_intermediate ‘ in function roc_curve can drop some suboptimal thresholds for creating lighter ROC curves. ( roc_curve ). all thresholds and corresponding TPRs and FPRs are calculated, but some of them are useless for plotting the ROC curve.
Which is the best value for the ROC curve?
In the above case, black circled point is the one which is closer to the ideal value and hence the threshold value corresponding to that point is the best probability threshold for this case. As summarized, ROC curve helps you in determining the right threshold value for your problem considering the variations of FPR and TPR values.
How to get the threshold of a curve?
Even more, I would like to get the class percentage threshold that was used to generate that point. the index number of the false positive rate ( x-value) that is closest to the threshold without being above it, should give me the index number of the appropriate true positive rate ( y-value ).
What does the ROC curve in scikit learn mean?
So, ROC curve is the curve where we plot TPR and FPR values of the results against different threshold values. Scikit learn library does a wonderful job in coming up with different threshold values and simplifies the FPR, TPR calculation for us. For the dataset, we worked upon — ROC curve looks as below: