这是一个完整的示例,使用生物信息学工具箱中的以下函数:SVMTRAIN http://www.mathworks.com/help/stats/svmtrain.html, 支持向量机分类 http://www.mathworks.com/help/stats/svmclassify.html, 一流的性能 http://www.mathworks.com/help/bioinfo/ref/classperf.html, 克罗斯瓦林德 http://www.mathworks.com/help/bioinfo/ref/crossvalind.html.
load fisheriris %# load iris dataset
groups = ismember(species,'setosa'); %# create a two-class problem
%# 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.
k=10;
cvFolds = crossvalind('Kfold', groups, k); %# get indices of 10-fold CV
cp = classperf(groups); %# init performance tracker
for i = 1:k %# for each fold
testIdx = (cvFolds == i); %# get indices of test instances
trainIdx = ~testIdx; %# get indices training instances
%# train an SVM model over training instances
svmModel = svmtrain(meas(trainIdx,:), groups(trainIdx), ...
'Autoscale',true, 'Showplot',false, 'Method','QP', ...
'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1);
%# test using test instances
pred = svmclassify(svmModel, meas(testIdx,:), 'Showplot',false);
%# evaluate and update performance object
cp = classperf(cp, pred, testIdx);
end
%# get accuracy
cp.CorrectRate
%# get confusion matrix
%# columns:actual, rows:predicted, last-row: unclassified instances
cp.CountingMatrix
输出:
ans =
0.99333
ans =
100 1
0 49
0 0
我们获得了99.33%
仅一个“setosa”实例被错误分类为“非 setosa”的准确性
UPDATE:SVM 函数已移至 R2013a 中的统计工具箱