Question

我试图在MATLAB中获得预测列矩阵，但我不太清楚如何编写它。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

问题在于它实际上总共计算了11次精确度 - 每次折叠10次，平均最后一次。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 ，那么可理解的准确性会大大降低。

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？

Answer 1

交叉验证的整体思路是对分类器的性能进行无偏估计。

完成后，您通常只需在整个数据上训练模型。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, ...);

如何使用MATLAB在SVM中获得预测值？

1 个答案: