svm - Matlab predict function not working

Question

I am trying to train a linear SVM on a data which has 100 dimensions. I have 80 instances for training. I train the SVM using fitcsvm function in MATLAB and check the function using predict on the training data. When I classify the training data with the SVM all the data points are being classified into only one class.

SVM = fitcsvm(votes,b,'ClassNames',unique(b)');
predict(SVM,votes);

This gives outputs as all 0's which corresponds to 0th class. b contains 1's and 0's indicating the class to which each data point belongs. The data used, i.e. matrix votes and vector b are given the following link

Answer 1

Make sure you use a non-linear kernel, such as a gaussian kernel and that the parameters of the kernel are tweaked. Just as a starting point:

SVM = fitcsvm(votes,b,'KernelFunction','RBF', 'KernelScale','auto');
bp = predict(SVM,votes);

that said you should split your set in a training set and a testing set, otherwise you risk overfitting