循环中的重复分类精度始终相同答案

【问题标题】：Repeated classification accuracies in a loop always the same循环中的重复分类精度始终相同
【发布时间】：2018-03-12 01:56:13
【问题描述】：

我有非常简单的二进制分类代码（见下文）。当我在 Matlab 中重新运行它时（只需手动按下“运行”按钮），每次运行都会为 14 个主题中的每一个提供略微不同的准确度。但是，如果我循环我的代码 nrPermute 次，循环的每次迭代都会为我提供与相应主题完全相同的准确性 - 为什么会这样？因此，在第一个代码中，不同运行的平均值（准确度）不同，而在第二个代码中，不同迭代的平均值（准确度）始终相同。以下两个代码

每个主题只进行一次 10 倍交叉验证的代码：

%% SVM-Classification
nrFolds = 10; %number of folds of crossvalidation, 10 is standard
kernel = 'linear'; % 'linear', 'rbf' or 'polynomial'
C = 1; 
solver = 'L1QP';

cvFolds = crossvalind('Kfold', labels, nrFolds);

for k = 1:14

for i = 1:nrFolds                            % iteratre through each fold
    testIdx = (cvFolds == i);                % indices of test instances
    trainIdx = ~testIdx;                     % indices training instances

    % train the SVM
    cl = fitcsvm(features(trainIdx,:), 
     labels(trainIdx),'KernelFunction',kernel,'Standardize',true,...
    'BoxConstraint',C,'ClassNames',[0,1],'Solver',solver);

    [label,scores] =  predict(cl, features(testIdx,:));
    eq = sum(label==labels(testIdx));
    accuracy(i) = eq/numel(labels(testIdx));

end

crossValAcc(k) = mean(accuracy);

end

每个 10 倍交叉验证重复 nrPermute 次的代码：

%% SVM-Classification
nrFolds = 10; %number of folds of crossvalidation, 10 is standard
kernel = 'linear'; % 'linear', 'rbf' or 'polynomial'
C = 1; 
solver = 'L1QP';

cvFolds = crossvalind('Kfold', labels, nrFolds);
nrPermute = 5;


for k = 1:14
for p = 1:nrPermute

for i = 1:nrFolds                            % iteratre through each fold
    testIdx = (cvFolds == i);                % indices of test instances
    trainIdx = ~testIdx;                     % indices training instances

    % train the SVM
    cl = fitcsvm(features(trainIdx,:), 
     labels(trainIdx),'KernelFunction',kernel,'Standardize',true,...
    'BoxConstraint',C,'ClassNames',[0,1],'Solver',solver);

    [label,scores] =  predict(cl, features(testIdx,:));
    eq = sum(label==labels(testIdx));
    accuracy(i) = eq/numel(labels(testIdx));

end

    accSubj(p) = mean(accuracy); % accuracy of each permutation

end

crossValAcc(k) = mean(accSubj);


end

【问题讨论】：

标签： matlab classification svm

【解决方案1】：

如果这对其他人也有用，我想办法：置换循环应该在 cvFolds = crossvalind('Kfold', labels, nrFolds); 之外。这样折叠的分布被重新洗牌！

【讨论】：