100% accuracy from libsvm

Question

I'm training and cross-validating (10-fold) data using libSVM (with linear kernel).

The data consist 1800 fMRI intensity voxels represented as a single datapoint. There are around 88 datapoints in the training-set-file for svm-train.

the training-set-file looks as follow:

+1 1:0.9 2:-0.2 ... 1800:0.1

-1 1:0.6 2:0.9 ... 1800:-0.98

...

I should also mention i'm using the svm-train script (came along with the libSVM package).

The problem is that when running svm-train - it's result as 100% accuracy!

This doesn't seem to reflect the true classification results! The data isn't unbalanced since

#datapoints labeled +1 == #datpoints labeled -1

Iv'e also checked the scaler (scaling correctly), and also tried to change the labels randomly to see how it impacts the accuracy - and it's decreasing from 100% to 97.9%.

Could you please help me understand the problem? If so, what can I do to fix it?

Thanks,

Gal Star

Answer 1

Make sure you include '-v 10' in the svmtrain option. I'm not sure your 100% accuracy comes from training sample or validation sample. It is very possible to get a 100% training accuracy since you have much less sample number than the feature number. But if your model suffers from overfitting, the validation accuracy may be low.