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 it’s 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 it’s 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

Can’t 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 it’s 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
Answer» B. We can not classify data correctly for given setting of hyper parameter C

Discussion

No Comment Found

Related MCQs

Reply to Comment