document: Data mining criteria for tree-based regression and classification This paper is concerned with the construction of regression and classification trees that are more adapted to data mining applications than conventional trees . To this end , we propose new splitting criteria for growing trees . Conventional splitting criteria attempt to perform well on both sides of a split by attempting a compromise in the quality of fit between the left and the right side . By contrast , we adopt a data mining point of view by proposing criteria that search for interesting subsets of the data , as opposed to modeling all of the data equally well . The new criteria do not split based on a compromise between the left and the right bucket ; they effectively pick the more interesting bucket and ignore the other . As expected , the result is often a simpler characterization of interesting subsets of the data . Less expected is that the new criteria often yield whole trees that provide more interpretable data descriptions . Surprisingly , it is a `` flaw '' that works to their advantage : The new criteria have an increased tendency to accept splits near the boundaries of the predictor ranges . This so-called `` end-cut problem '' leads to the repeated peeling of small layers of data and results in very unbalanced but highly expressive and interpretable trees .
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