Exporting model to classify new data

5 次查看（过去 30 天）
Uerm
Uerm 2019-12-18
评论： Uerm 2020-1-10
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:
  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?

请先登录，再回答此问题。

采纳的回答

Ridwan Alam
Ridwan Alam 2019-12-18
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 个评论
显示 7更早的评论
Ridwan Alam
Ridwan Alam 2020-1-6
编辑：Ridwan Alam 2020-1-6
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
Uerm 2020-1-10
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?

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Statistics and Machine Learning Toolbox 的更多信息

标签

产品


版本

R2019b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by