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## 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. | can�t say |

D. | none of these |

Answer» A. we can still classify data correctly for given setting of hyper parameter c |

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