[amp_mcq option1=”the decision boundry in the transformed feature space in non-linear” option2=”the decision boundry in the transformed feature space in linear” option3=”the decision boundry in the original feature space in not considered” option4=”the decision boundry in the original feature space in linear” correct=”option1″]
The correct answer is: A. the decision boundry in the transformed feature space in non-linear.
In SVM, the RBF kernel is a non-linear kernel that can be used to map the data into a higher dimensional space where the data is linearly separable. In this scenario, the decision boundary in the transformed feature space will be non-linear.
The other options are incorrect because:
- Option B is incorrect because the decision boundary in the transformed feature space is non-linear.
- Option C is incorrect because the decision boundary in the original feature space is not considered in SVM.
- Option D is incorrect because the decision boundary in the original feature space is linear.