1. A method of training a traffic classifier, the method comprising: generating, by a server, a training data set comprising a plurality of training flow sets, each training flow set comprising one of a plurality of training feature sets and an associated one of a plurality of class labels, each training feature set and associated class label based on measurements of one of a plurality of training packet flows, the associated class label identifying to which one of a plurality of traffic classes the one of the plurality of training packet flows belongs; training, by the server, a plurality of binary classifiers using a reduced training data set that is generated from the training data set, each binary classifier associated with one of the plurality of traffic classes, each binary classifier configured to generate an output score based on the reduced training data set and based on measurements of a packet flow, wherein for each of the plurality of binary classifiers the reduced training data set comprises all training features sets associated with the one of the plurality of traffic classes that are contained in the training data set and training packet flows associated with other traffic classes of the plurality of traffic classes that are uniformly sampled from the training data set; and training, by the server, a plurality of calibrators using the training data set, wherein each calibrator is associated with one of the plurality of binary classifiers, wherein the training the plurality of calibrators comprises training each of the plurality of calibrators to translate the output score of the associated binary classifier into an estimated class probability value, the estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator.