oint 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. Based upon that give the answer for following question. What would happen when you use very large value of C(C->infinity)? Note: For small C was also classifying all data points correctly Group of answer choices a. We can still classify data correctly for given setting of hyper parameter C b. We can not classify data correctly for given settin
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. Based upon that give the answer for following question.
What would happen when you use very large value of C(C->infinity)?
Note: For small C was also classifying all data points correctly
Given: Suppose you are building an 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 an SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameters. Based upon that give the answer for the following question.
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