Make predictions on new data using a SVM
3 visualizzazioni (ultimi 30 giorni)
Mostra commenti meno recenti
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
0 Commenti
Risposta accettata
Più risposte (0)
Vedere anche
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!