My first question is sensitivity of SVM to unbalanced datapoints. How much SVM is sensitive to that?
And is there any functionality designed i the fitcsvm to account for the unbalance in the datapoints in binary classification? I know that oversampling the smaller class or undersampling the larger class can be a solution to deal with "unbalanced" observation but I am interested for other approaches.
I checke "prior" and found it's role is only to remove observations with zero prior probablity and apparently doesnot play role in the classification step.