Is SVM sensitive to unbalanced observations? The observations in one class is 3-4 times of the observation in an other class in binary classification
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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.
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Answers (1)
Prince Kumar
on 22 Nov 2021
Hi Zeynab Mousavikhamene,
Yes, SVM is sensitive to imbalanced dataset and this gives suboptimal models.
You can use 'Cost' Name-Value pair and pass a cost matrix. fitcsvm uses the input cost matrix to adjust the prior class probabilities.
You can refer the following link for more information :
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