# How to perform stratified 10 fold cross validation for classification in MATLAB?

15 views (last 30 days)
Commented: uma on 9 May 2022
My implementation of usual K-fold cross-validation is pretty much like:
K = 10;
CrossValIndices = crossvalind('Kfold', size(B,2), K);
for i = 1: K
display(['Cross validation, folds ' num2str(i)])
IndicesI = CrossValIndices==i;
TempInd = CrossValIndices;
TempInd(IndicesI) = [];
xTraining = B(:, CrossValIndices~=i);
tTrain = T_new1(:, CrossValIndices~=i);
xTest = B(:, CrossValIndices ==i);
tTest = T_new1(:, CrossValIndices ==i);
end
But To ensure that the training, testing, and validating dataset have similar proportions of classes (e.g., 20 classes).I want use stratified sampling technique.Basic purpose is to avoid class imbalance problem.I know about SMOTE technique but i want to apply this one.
##### 3 CommentsShow 1 older commentHide 1 older comment
Leyre Azcárate Bescós on 27 Aug 2020
And T_new1?
uma on 9 May 2022
what is T_new1

Tom Lane on 25 Jul 2017
If you have the Statistics and Machine Learning Toolbox, consider the cvpartition function. It can define stratified samples.
##### 3 CommentsShow 1 older commentHide 1 older comment
Olympia Gallou on 6 May 2021
What is the B and T_new?
Olympia Gallou on 6 May 2021
How did you solve your problem?

ashik khan on 18 Nov 2018
What are the value of B and T_new1 ??
K = 10;
CrossValIndices = crossvalind('Kfold', size(B,2), K);
for i = 1: K
display(['Cross validation, folds ' num2str(i)])
IndicesI = CrossValIndices==i;
TempInd = CrossValIndices;
TempInd(IndicesI) = [];
xTraining = B(:, CrossValIndices~=i);
tTrain = T_new1(:, CrossValIndices~=i);
xTest = B(:, CrossValIndices ==i);
tTest = T_new1(:, CrossValIndices ==i);
end

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