Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
This is the square root of the average squared loss for the class probability estimates generated by the classifier (see the data mining book).
Note that the squared loss for a particular test instance is the sum of the squared differences over all the classes. The observed probabilities for a test instance are 0/1 probabilities, with a 1 for the class that actually occurs in the data, and 0 for all the other classes.
Source: Weka FAQ