additional considerations binary versus non-binary trees while it is possible to split tree node into more than two groups (multiway splits)it generally produces inferior results compared to the simple binary split the major reason is that multiway splits can lead to too many nodes near the tree root that have only few data pointsthus leaving insufficient data for later splits as multiway splits can be represented as several binary splitsthe latter is preferred [ data preprocessing sometimesit can be beneficial to preprocess the data prior to the tree construction for examplepca can be used with view to identify the most important dimensionswhich in turn will lead to simpler and possibly more informative splitting rules in the internal nodes alternative splitting rules we restricted our attention to splitting rules of the type ( { }where { pand these types of rules may not always result in simple partition of the feature spaceas illustrated by the binary data in figure in this casethe feature space could have been partitioned into just two regionsseparated by straight line figure the two groups of points can here be separated by straight line insteadthe classification tree divides up the space into many rectanglesleading to an unnecessarily complicated classification procedure in this case many classification methods discussed in such as linear discriminant analysis (section )will work very wellwhereas the classification tree is rather elaboratedividing the feature set into too many regions an obvious remedy is to use splitting rules of the form ( {ax