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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 small C (C∼0)?
Data will be correctly classified
Misclassification would happen
Can't say
None of these
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