CODEWITHSUNDEEP
machine-learningregressionsvmlibsvm
vinzzz
vinzzz

Reputation: 2104

libsvm not giving support vectors / no support vectors

I am using jlibsvm to do SVM for regression .My data set is very small (42 samples) . When I use the dataset to create the model using epsilon SVR with sigmoid kernel then no support vectors are generated.

This is what I get in my model file :

svm_type epsilon_svr
kernel_type sigmoid
gamma 0.02380952425301075
coef0 0.0
label
rho -66.42803
total_sv 0
probA -1.0
SV

When I use some other data set on the libsvm website I get a model file with support vectors fine. Can someone please suggest why no support vectors are being generated for my data set ? My data set file is formatted right so no issues there...

Upvotes: 2

Views: 986

Answers (1)

Nicolas78
Nicolas78

Reputation: 5144

This could mean that the best found classification, given your data and the hyperparameters, is to assign the same label to all samples.

  • Are your samples unbalanced? What's the number of positive and negative samples? You might want to try to add a weighting to positive/negative samples to account for that

  • It could also be the samples are hard to separate given their structure and the kernel type. Have you tried a different structure?

With only 42 data samples, maybe you could add them to your question and get better answers.

Upvotes: 1

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