The SVM method is not recommended for huge data sets. We must select an ideal kernel for SVM, which is a challenging process. Furthermore, SVM performs poorly when the number of training data samples is smaller than the number of features in each data set. Since the support vector machine is not a probabilistic model, we are unable to explain the classification in terms of probability. Moreover, the algorithmic complexity and memory requirements of SVM are quite high.