[Solved] How can SVM be classified?

Q.	How can SVM be classified?
A.	it is a model trained using unsupervised learning. it can be used for classification and regression.
B.	it is a model trained using unsupervised learning. it can be used for classification but not for regression.
C.	it is a model trained using supervised learning. it can be used for classification and regression.
D.	t is a model trained using unsupervised learning. it can be used for classification but not for regression.
Answer» C. it is a model trained using supervised learning. it can be used for classification and regression.

1.5k

Do you find this helpful?

View all MCQs in

Machine Learning (ML)

Discussion

No comments yet

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?
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)?
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)?
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-