In the case of linear models, data can be easily classified by drawing a straight line, which is not the case with non-linear support vector machine models. Linear SVMs are faster to train when compared to non-linear SVMs. A linear SVM algorithm presupposes linear separability for each data point. While in a non-linear SVM, the software transforms the data vectors using the best nonlinear kernel function for the given circumstance.