Support Vector Machine, Kernel Trick, Svm, Soft Margin Svm, Kernel.
most useful when the data is non-linearly separable. If the dataset is not linearly separable then we need to use kernel trick which conceptually maps the datapoints to some higher dimensional space. The most widely used kernels in SVM are linear kernel, polynomial kernel and radial basis kernel.