What is the purpose of "kernels" in SVM?
A) Kernels try to linearize nonlinear problems and make a solution possible.
B) Kernels optimize model selection (selection of the best variables)
C) Kernels visualize SVM results
D) Kernels are the source code underlying SVM

Question

Quizplus · Accepted Answer

Kernels are used in SVM to transform the input data into a higher-dimensional space, where a linear separation of the data is possible. This is particularly useful when dealing with nonlinearly separable data. Kernels function as a similarity function between points in this higher-dimensional space, allowing the SVM to find the optimal hyperplane that separates the data points. Therefore, A is the correct choice. B, C, and D are not accurate descriptions of the purpose of kernels in SVM.

What Is the Purpose of "Kernels" in SVM