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0. 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)?
We can still classify data correctly for given setting of hyper parameter C.
We can not classify data correctly for given setting of hyper parameter C.
Can't say
None of the above
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