next up previous contents Next: Inference algorithm Up:
Introduction Previous: Introduction   Contents  Basic
assumptions  The classification task consist of classifying a
variable $y=x_0$ called the class variable given a set of
variables ${\bf x} = x_1\ldots x_n$, called attribute variables.
A classifier $h:{\bf x}\to y$ is a function that maps an
instance of $\bf x$ to a value of $y$. The classifier is learned
from a dataset $D$ consisting of samples over $({\bf x}, y)$.
The learning task consists of finding an appropriate Bayesian
network given a data set $D$ over $U$.  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  Remco Bouckaert 2008-05-12