Exporting model to classify new data
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Uerm
el 18 de Dic. de 2019
Comentada: Uerm
el 10 de En. de 2020
Hi,
I have attached the code I use to classify my data. I use 16 different models. What I want to do is the following:
- I want to save/export the model sort of like the Classification Learner app does in order to make predictions on new data.
- I want to make a ROC curve with AUC results for each of the models
How can I do that?
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Ridwan Alam
el 18 de Dic. de 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
tic
classificationLinearSVM = fitcsvm(...
trainingData(train,1:end-1),...
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;
save('classificationLinearSVM.mat','classificationLinearSVM','-v7.3');
% 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!
9 comentarios
Ridwan Alam
el 6 de En. de 2020
Editada: Ridwan Alam
el 6 de En. de 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!
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