我正在尝试使用 libsvm (带有 Matlab 接口(interface))来运行一些多标签分类问题。这是使用 IRIS 数据的一些玩具问题:
load fisheriris;
featuresTraining = [meas(1:30,:); meas(51:80,:); meas(101:130,:)];
featureSelectedTraining = featuresTraining(:,1:3);
groundTruthGroupTraining = [species(1:30,:); species(51:80,:); species(101:130,:)];
[~, ~, groundTruthGroupNumTraining] = unique(groundTruthGroupTraining);
featuresTesting = [meas(31:50,:); meas(81:100,:); meas(131:150,:)];
featureSelectedTesting = featuresTesting(:,1:3);
groundTruthGroupTesting = [species(31:50,:); species(81:100,:); species(131:150,:)];
[~, ~, groundTruthGroupNumTesting] = unique(groundTruthGroupTesting);
% Train the classifier
optsStruct = ['-c ', num2str(2), ' -g ', num2str(4), '-b ', 1];
SVMClassifierObject = svmtrain(groundTruthGroupNumTraining, featureSelectedTraining, optsStruct);
optsStruct = ['-b ', 1];
[predLabelTesting, predictAccuracyTesting, ...
predictScoresTesting] = svmpredict(groundTruthGroupNumTesting, featureSelectedTesting, SVMClassifierObject, optsStruct);
但是,对于我得到的预测概率(此处显示前 12 行结果)
1.08812899093155 1.09025554950852 -0.0140009056912001
0.948911671379753 0.947899227815959 -0.0140009056926024
0.521486301840914 0.509673405799383 -0.0140009056926027
0.914684487894784 0.912534150299246 -0.0140009056926027
1.17426551505833 1.17855350325579 -0.0140009056925103
0.567801459258613 0.557077025701113 -0.0140009056926027
0.506405203427106 0.494342606399178 -0.0140009056926027
0.930191457490471 0.928343421250020 -0.0140009056926027
1.16990617214906 1.17412523596840 -0.0140009056926026
1.16558843984163 1.16986137054312 -0.0140009056926015
0.879648874624610 0.876614924593740 -0.0140009056926027
-0.151223818963057 -0.179682730685229 -0.0140009056925999
我很困惑,为什么有些概率大于 1,有些概率却为负?
但是,预测的标签似乎相当准确:
1
1
1
1
1
1
1
1
1
1
1
3
最终输出
Accuracy = 93.3333% (56/60) (classification)
那么如何解释预测概率的结果呢?多谢。答:
最佳答案
支持向量机的输出不是概率!
分数的符号表示它属于 A 类还是 B 类。如果分数是 1 或 -1,则它处于边缘,尽管知道这一点并不是特别有用。
如果您确实需要概率,可以使用 Platt scaling 进行转换。 。您基本上对它们应用了 sigmoid 函数。
关于matlab - 多标签分类的 libsvm 输出预测概率,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/27278486/