I am working on an application to evaluate the performance of SVM on a number of different datasets.

SVM is working well, yielding 98% diagonal accuracy.

I now need to evaluate the performance of this model, so using ROCR library, I plotted the ROC, err & acc curves.

The ROC is displayed as the False Positive Rate vs. True Positive Rate, and for the error & accuracy curves, these are plotted against an element called cutoff.

Could someone explain what this cutoff is exactly? (Needless to say, I am relatively new to this subject)

Thanks in advance

enter image description here


1 Answer 1


Usually, the predicted values in classifiers for a binary response variable is interpreted as a probability which shows the observation belongs to class 1 or not.

To make a decision at this point, a cutoff value is used, i.e. if the probability is greater than the cutoff, it belongs to class 1 and if the probability is lower than cutoff it, belongs to class 2.

For more information, please see this free book: "The Elements of Statistical Learning".


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.