McqMate
Sign In
Hamberger menu
McqMate
Sign in
Sign up
Home
Forum
Search
Ask a Question
Sign In
McqMate Copyright © 2024
→
Computer Science Engineering (CSE)
→
Machine Learning (ML)
→
SVM is a learning
Q.
SVM is a learning
A.
supervised
B.
unsupervised
C.
both
D.
none
Answer» A. supervised
1.5k
0
Do you find this helpful?
8
View all MCQs in
Machine Learning (ML)
Discussion
No comments yet
Login to comment
Related MCQs
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
he minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?
The soft margin SVM is more preferred than the hard-margin SVM when-
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 large value of C(C->infinity)?
The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM�s?
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