3. What do we achieve by kernel trick in case of SVM classifier? Can we use this trick for arbitrary dimensions? 4. Suppose you have trained an SVM classifier with a Gaussian kernel, and it learned the following decision boundary on the training set: ca 05 . 057 0 3, 02 You suspect that the SVM is under fitting your dataset. Should you try increasing or decreasing C? Increasing or decreasing Gamma