Multidimensional Decision Tree Splits to Improve Interpretability

Abstract

Abstract We revisit the binary splitting functionality used in decision trees to handle numerical attributes. Even if the true relationship between the class label and a few numerical attributes can be expressed directly (using a Boolean expression), resulting decision trees may appear quite large and complicated. In cases where interpretability is important, an increased computational effort on the splitting criteria that offers more compact trees might be worthwhile. We propose and empirically evaluate multidimensional splits, where a tree node may test for inclusion in a low-dimensional bounding box.