Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very large value of C(C->infinity)?

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Q. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very large value of C(C->infinity)?

(A) we can still classify data correctly for given setting of hyper parameter c
(B) we can not classify data correctly for given setting of hyper parameter c
(C) can�t say
(D) none of these

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✅ Correct Answer: (A) we can still classify data correctly for given setting of hyper parameter c

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