The classification task consist of classifying a variable called the class variable given a set of variables , called attribute variables. A classifier is a function that maps an instance of to a value of . The classifier is learned from a dataset consisting of samples over . The learning task consists of finding an appropriate Bayesian network given a data set over .
All Bayes network algorithms implemented in Weka assume the following for the data set:
The first step performed by buildClassifier is checking if the data set fulfills those assumptions. If those assumptions are not met, the data set is automatically filtered and a warning is written to STDERR.2