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Based on the readme file in the libsvm package. The svmpredict matlab version function is used in the following way:

[predicted_label, accuracy, decision_values/prob_estimates] =
svmpredict(testing_label_vector, testing_instance_matrix, model [,
'libsvm_options']);

The return value "accuracy" is always a three dimension vector. The first element in the vector is the classification accuracy. What are the other two?

Can I use the decision_values as the confidence score? Or prob_estimates?

Thanks so much.

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If you read down in the README a bit:

Result of Prediction

The function 'svmpredict' has three outputs. The first one, predictd_label, is a vector of predicted labels. The second output, accuracy, is a vector including accuracy (for classification), mean squared error, and squared correlation coefficient (for regression).

If you're doing classification, you should only care about the first component.

decision_values are confidence scores, by definition. They represent how far past the margin the test points lie. prob_estimates, when using -b 1 in the libsvm_options to turn them on, are monotonic transformations of decision values (which can be any real number, where 0 is the dividing line between the classes) to probabilities (between 0 and 1, with 0.5 the dividing line).

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  • $\begingroup$ Exactly this. The manual is clear about these things (+1). $\endgroup$ Feb 26, 2015 at 8:40

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