MATLAB: Make predictions on new data using a SVM

svm

I trained a SVM classifcation model using "fitcsvm" function and tested with the test data set. Now I want to use this model to predict the classes of new (previously unseen) data. What should be done to predict new data ?
Following is the code I used.
   load FeatureLabelsNum.csv
   load FeatureOne.csv
   X = FeatureOne(1:42,:);
   y = FeatureLabelsNum(1:42,:);
   %dividing the dataset into training and testing 
   rand_num = randperm(42);
   %training Set
   X_train = X(rand_num(1:34),:);
   y_train = y(rand_num(1:34),:);
   %testing Set
   X_test = X(rand_num(34:end),:);
   y_test = y(rand_num(34:end),:);
   %preparing validation set out of training set
   c = cvpartition(y_train,'k',5);
   SVMModel =
   fitcsvm(X_train,y_train,'Standardize',true,'KernelFunction','RBF',...'KernelScale','auto','OutlierFraction',0.05);
   CVSVMModel = crossval(SVMModel);
   classLoss = kfoldLoss(CVSVMModel)
   classOrder = SVMModel.ClassNames
   sv = SVMModel.SupportVectors;
   figure
   gscatter(X_train(:,1),X_train(:,2),y_train)
   hold on
   plot(sv(:,1),sv(:,2),'ko','MarkerSize',10)
   legend('Resampled','Non','Support Vector')
   hold off

Best Answer

Hi,
use the
predict
command for this purpose. See the documentation for predict command for examples how to do.
Best regards
Stephan
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