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Exporting model to classify new data

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Uerm on 18 Dec 2019
Commented: Uerm on 10 Jan 2020
I have attached the code I use to classify my data. I use 16 different models. What I want to do is the following:
  1. I want to save/export the model sort of like the Classification Learner app does in order to make predictions on new data.
  2. I want to make a ROC curve with AUC results for each of the models
How can I do that?


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Accepted Answer

Ridwan Alam
Ridwan Alam on 18 Dec 2019
1.Save: (assuming you want to save/export each classifier in separate files) use save().
2. ROC curve: use perfcurve() and plot() with hold on;
% Linear SVM
classificationLinearSVM = fitcsvm(...
trainingData(train,end), ...
'KernelFunction', 'linear', ...
'PolynomialOrder', [], ...
'KernelScale', 'auto', ...
'BoxConstraint', 1, ...
'Standardize', true, ...
'ClassNames', [0; 1]);
[predsLinSVM,~] = predict(classificationLinearSVM,trainingData(test,1:end-1));
targetLinSVM = trainingData(test,end);
targetsLinSVM_all = [targetsLinSVM_all; squeeze(targetLinSVM)];
predsLinSVM_all = [predsLinSVM_all; squeeze(predsLinSVM)];
t1 = toc;
% you need to declare the posclass
[~,scoresLinSVM] = resubPredict(fitPosterior(classificationLinearSVM));
[xLinSVM,yLinSVM,~,aucLinSVM] = perfcurve(trainingData(train,end),scoresLinSVM(:,2),posclass);
plot(xLinSVM,yLinSVM); hold on;
Hope this helps!


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Uerm on 6 Jan 2020
Ok, it makes total sense. Regarding the save part... Does it mean that I have to have 10 save commands for each model since it keeps over-writing?
Ridwan Alam
Ridwan Alam on 6 Jan 2020
Say, for the SVM models, if you really want to save the 10 SVM models from each iteration, you can either give them a new name in each iteration (eg mySvm_1, mySvm_2, ...) and save all of them after exiting the loop. But, again, I don't think that's very common to save the intermediate models from all the iterations of the cross-validation. Good luck.
Btw, if you liked the conversation, please vote up the response. Thanks!
Uerm on 10 Jan 2020
Hi Ridwan,
Thanks a lot, I voted up the response!
I have run into another problem (I have attached the code). When I plot the confusion matrix and ROC curve, it seems that the results from the training and validation are combined into one. What I mean by this is that for instance in the confusion matrix, when the numbers in the matrix is summed, it is exactly equal to all the samples (training samples + validation samples). I want to have two confusion matrices (and two ROC curves and thus 2 AUC values) for every model --> One for the training and one for the validation. Is that possible?

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