[Solved] What is/are true about kernel in SVM? 1. Kernel function map low dimensional data to high dimensional space 2. It’s a similarity function

Q.	What is/are true about kernel in SVM? 1. Kernel function map low dimensional data to high dimensional space 2. It’s a similarity function
A.	1
B.	2
C.	1 and 2
D.	none of these
Answer» C. 1 and 2

1.5k

Do you find this helpful?

View all MCQs in

Machine Learning (ML)

Discussion

No comments yet

Related MCQs

What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It�s a similarity function
What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function
Which of the following is true about SVM? 1. Kernel function map low dimensional data to high dimensional space. 2. It is a similarity Function
In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.
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?
We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables 3. Feature normalization always helps when we use Gaussian kernel in SVM
We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1.We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables 3. Feature normalization always helps when we use Gaussian kernel in SVM
We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM