• labels : An m by 1 vector of prediction labels. If labels of test data are unknown, simply use any random values. (type must be double)
• data : An m by n matrix of m testing instances with n features. It can be dense or sparse. (type must be double)
• model : The output of svmtrain function.
• libsvm_options : A string of testing options in the same format as that of LIBSVM.

libsvm_options :

• -b : probability_estimates; whether to predict probability estimates.

• -q : quiet mode. (no outputs)

The svmpredict function has three outputs. The first one, predicted_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). The third is a matrix containing decision values or probability estimates (if -b 1’ is specified). If $k$ is the number of classes in training data, for decision values, each row includes results of predicting $k(k-1)/2$ binary-class SVMs. For classification, $k\; =\; 1$ is a special case. Decision value +1 is returned for each testing instance, instead of an empty vector. For probabilities, each row contains $k$ values indicating the probability that the testing instance is in each class. Note that the order of classes here is the same as Label field in the model structure.

$Note\; on\; LIBSVM\; 3.36\; Update$: This implementation is based on LIBSVM 3.36 (2025) and now supports probability estimates for One-Class SVM (-s 2) when combined with the probability flag (-b 1). For One-Class SVM, the prob_estimates output is a single column vector containing the probability of the instance being an inlier.

Source Code: svmpredict