MATLAB Answers

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

Latest activity Answered by ashik khan on 18 Nov 2018
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.

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2 Answers

Answer by Tom Lane
on 25 Jul 2017

If you have the Statistics and Machine Learning Toolbox, consider the cvpartition function. It can define stratified samples.

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yes,i have Statistics and Machine Learning Toolbox.But,how can i implement cvpartition in below code
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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Answer by 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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