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

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Machine Learning Enthusiast 2017-7-21

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https://ww2.mathworks.cn/matlabcentral/answers/349671-how-to-perform-stratified-10-fold-cross-validation-for-classification-in-matlab

评论： uma 2022-5-9

采纳的回答： Tom Lane

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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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Leyre Azcárate Bescós 2020-8-27

And T_new1?

uma 2022-5-9

what is T_new1

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采纳的回答

Tom Lane 2017-7-25

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

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Olympia Gallou 2021-5-6

What is the B and T_new?

Olympia Gallou 2021-5-6

How did you solve your problem?

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更多回答（1 个）

ashik khan 2018-11-18

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/349671-how-to-perform-stratified-10-fold-cross-validation-for-classification-in-matlab#answer_347664

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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