[Solved] Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?

Q.	Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?
A.	the model would consider even far away points from hyperplane for modeling
B.	the model would consider only the points close to the hyperplane for modeling
C.	the model would not be affected by distance of points from hyperplane for modeling
D.	none of the above
Answer» B. the model would consider only the points close to the hyperplane for modeling

1.3k

Do you find this helpful?

View all MCQs in

Machine Learning (ML)

Discussion

No comments yet

Related MCQs

Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?
Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?
Suppose you are using RBF kernel in SVM with high Gamma valu
Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting. Which of the following is best option would you more likely to consider iterating SVM next time?
Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?
Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?
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.What would happen when you use very large value of C(C->infinity)?
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.What would happen when you use very large value of C(C->infinity)?
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.What would happen when you use very small C (C~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 it’s hyper parameter.What would happen when you use very small C (C~0)?