Make predictions on new data using a 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

Answer 1

[Stephan]

Hi,

use the

predict

command for this purpose. See the documentation for predict command for examples how to do.

Best regards

Stephan

[NC]

Thank you very much