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

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Machine Learning Enthusiast
评论： uma 2022-5-9
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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采纳的回答

Tom Lane
Tom Lane 2017-7-25
If you have the Statistics and Machine Learning Toolbox, consider the cvpartition function. It can define stratified samples.
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更多回答（1 个）

ashik khan
ashik khan 2018-11-18
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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