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我正在尝试在 MATLAB 中获取预测列矩阵，但我不太清楚如何对其进行编码。我目前的代码是 -

load DataWorkspace.mat
groups = ismember(Num,'Yes');
k=10;

%# number of cross-validation folds:
%# If you have 50 samples, divide them into 10 groups of 5 samples each,
%# then train with 9 groups (45 samples) and test with 1 group (5 samples).
%# This is repeated ten times, with each group used exactly once as a test set.
%# Finally the 10 results from the folds are averaged to produce a single 
%# performance estimation.

cvFolds = crossvalind('Kfold', groups, k);
cp = classperf(groups);
for i = 1:k                               
    testIdx = (cvFolds == i);               
    trainIdx = ~testIdx;                   
    svmModel = svmtrain(Data(trainIdx,:), groups(trainIdx), ...
                 'Autoscale',true, 'Showplot',false, 'Method','SMO', ...
                 'Kernel_Function','rbf');

    pred = svmclassify(svmModel, Data(testIdx,:), 'Showplot',false);

    %# evaluate and update performance object
    cp = classperf(cp, pred, testIdx);
end
cp.CorrectRate
cp.CountingMatrix

问题是它实际上总共计算了 11 次准确度——每次折叠 10 次，最后一次平均计算。但是，如果我对每个折叠进行单独的预测并为每个循环打印pred，那么可以理解的准确性就会大大降低。

但是，我需要每行数据的预测值的列矩阵。关于如何修改代码的任何想法？

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1 回答 1

1

交叉验证的整个想法是对分类器的性能进行无偏估计。

一旦完成，您通常只需在整个数据上训练一个模型。该模型将用于预测未来的实例。

所以就这样做：

svmModel = svmtrain(Data, groups, ...);
pred = svmclassify(svmModel, otherData, ...);
于 2013-05-06T22:27:26.860 回答