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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