In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario

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.