如何使用MATLAB在SVM中获得预测值？

Question

我想在MATLAB中得到一个预测列矩阵，但我不知道如何去编码它。我当前的代码是 -

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 

的问题是，它的实际计算精度共11次 - 10次，每次倍和最后一个时间的平均值。但是，如果我对每个回路进行单独的预测并为每个回路打印pred，则可以理解的精度大大降低。

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

交叉验证的整个思想是获得分类器性能的无偏估计。

一旦完成，您通常只是在整个数据上训练一个模型。这个模型将被用来预测未来的情况。

所以只是做：

svmModel = svmtrain(Data, groups, ...); 
pred = svmclassify(svmModel, otherData, ...);