Take the 2-minute tour ×
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It's 100% free, no registration required.

I'm using rb-libsvm and the RBF kernel to make classifications. svm.predict(measurements) returns either -1.0 or 1.0. Is there a way to get a confidence for this classification? I am interested in throwing out low-confidence classifications and tweaking precision/recall.

share|improve this question
add comment

2 Answers

up vote 1 down vote accepted

There's a method called predict_probability which returns a distribution for the prediction

share|improve this answer
add comment

I don't have much experience with the specific implementation of SVM you are using, however in general there are a range of methods for calibrating the output of an SVM classifier into probabilities. For example, the R library 'kernlab' has an option to fit a probability model to the distances of points from the support vectors via a sigmoid function. This can then be used to predict probabilities instead of just the best guess classes.

An interesting discussion of SVM calibration and optimisation of F1 scores can be found here. In general, the key concept to search for is calibration of classifier output.

share|improve this answer
add comment

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.