Q) What are Different Kernels in SVM? Ans: There are six type | datascienceinfo

Q) What are Different Kernels in SVM?

Ans: There are six types of kernels in SVM:
Linear kernel - used when data is linearly separable.
Polynomial kernel - When you have discrete data that has no natural notion of smoothness.
Radial basis kernel - Create a decision boundary able to do a much better job of separating two classes than the linear kernel.
Sigmoid kernel - used as an activation function for neural networks.