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What is one advantage of using kernels with support vector machines rather than simply applying a support vector classifier to an enlarged feature
What is one advantage of using kernels with support vector machines rather than simply applying a support vector classifier to an enlarged feature space (containing squared predictor terms, interactions, etc.)?
a) Using kernels with support vector machines allows non-linear decision boundaries whereas applying a support vector classifier to an enlarged feature space constrains the decision boundary to be linear.
b) Using kernels with support vector machines always results in better predictive performance on the test set than applying a support vector classifier to an enlarged feature space.
c) Using kernels with support vector machines is more computationally efficient than applying a support vector classifier to an enlarged feature space.
