)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"

🎲 Try a Random Question  |  Total Questions in Quiz: 75  |  🧠 Study this quiz with Flashcards
This question is part of a full practice quiz:
Machine Learning: Classification and Clustering — practice the complete quiz, review flashcards, or try a random 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. 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"