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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Zeynab Mousavikhamene 2020년 5월 19일
답변: Prince Kumar 2021년 11월 22일
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.
Prince Kumar 2021년 11월 22일
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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