visTree: Visualization of Subgroups for a Decision Tree

TL;DR

visTree introduces a new visualization method for decision trees that emphasizes subgroup characterization over individual splits, aiding interpretation of outcome-covariate relationships.

Contribution

The paper presents visTree, a novel visualization approach and R package that enhances decision tree interpretability by focusing on subgroups rather than split points.

Findings

Effective visualization of subgroups in decision trees.

Application demonstrated on health study data.

Enhanced interpretability of decision tree models.

Abstract

Decision trees are flexible prediction models which are constructed to quantify outcome-covariate relationships and characterize relevant population subgroups. However, the standard graphical representation of fitted decision trees highlights individual split points, and hence is suboptimal for visualizing defined subgroups. In this paper, we present a novel visual representation of decision trees which shifts the primary focus to characterizing subgroups, both in terms of their defining covariates and their outcome distribution. We implement our method in the \texttt{visTree} package, which builds on the toolkit and infrastructure provided by the \texttt{partykit} package and enables the visualization to be applied to varied decision trees. Individual functions are demonstrated using data from the Box Lunch study [French et al., 2014], a randomized trial to evaluate the effect of exposure to different lunch sizes on energy intake and body weight among working adults.