C443: a Methodology to See a Forest for the Trees

Often tree-based accounts of statistical learning problems yield multiple decision trees which together constitute a forest. Reasons for this include examining tree instability, improving prediction accuracy, accounting for missingness in the data, and taking into account multiple outcome variables. A key disadvantage of forests, unlike individual decision trees, is their lack of transparency. Hence, an obvious challenge is whether it is possible to recover some of the insightfulness of individual trees from a forest. In this paper, we will propose a conceptual framework and methodology to do so by reducing forests into one or a small number of summary trees, which may be used to gain insight into the central tendency as well as the heterogeneity of the forest. This is done by clustering the trees in the forest based on similarities between them. By means of simulated data, we will demonstrate how and why different similarity types in the proposed methodology may lead to markedly different conclusions, and explain when and why certain approaches may be recommended over other ones. We will finally illustrate the methodology with an empirical data set on the prediction of cocaine use on the basis of personality characteristics.