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)?

Question

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)?

TuteeHUB · Accepted Answer

we can not classify data correctly for given setting of hyper parameter c

TuteeHUB · Answer

we can still classify data correctly for given setting of hyper parameter c

TuteeHUB · Answer

cant say

TuteeHUB · Answer

none of these

1.	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.	cant say
D.	none of these
Answer» B. we can not classify data correctly for given setting of hyper parameter c

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