\begin{table}[p]
\bd
\item[Naive Bayes] can use a normal distribution, a Kernel estimation, or supervised discretization to discretize numeric attributes. We examine the normal distribution to accomplish discretization.
\item[C4.5] can set a minimum number of instances per leaf node and set the confidence threshold for pruning. We examine a minimum of 2 instances per leaf node and a confidence threshold of 0.25.
\item[OneR] can set the minimum number of instances per rule. We examine a minimum of 6 instances per rule.
\item[LWL] can set the kernel shape to use for weighting and the number of nearest neighbors to include in the kernel shape. We examine a linear kernel, and a variety of nearest neighbors. This is detailed in \tion{lwlOptions}.
\item[RIPPER] can choose the quantity of data to use for pruning, the minimum number of instances per rule, and the number of optimization runs to perform. We examine the default values of $\frac{1}{3}$ of the data for pruning, a minimum of 2 instances per rule, and 2 optimization runs to reduce error.
\item[Ridor] can choose the quantity of data to use for pruning and the minimum number of instances per rule. We examine the default values of $\frac{1}{3}$ of the data for pruning and a minimum of 2 instances per rule.
\item[Clump] can choose the the minimum number of instances per node and the threshold of mixedupedness until a node attempts to split. We examine a minimum of 5 instances per node, and a threshold of 5 non-majority class instances. These values were arrived at via experimentation.
\ed

\caption{Configuration Options for the Classification Algorithms}
\label{tbl:configOptions}
\end{table}
