Colorful Trees: Visualizing Random Forests for Analysis and Interpretation

Abstract

Random Forests (RFs) are a powerful machine learning technique used for various applications including classification, regression, clustering, and manifold learning. The interpretation of a given Random Forest usually relies on statistical values, such as the distribution of path length, leaf impurity, leaf size, etc. All those measures focus on specific aspects and are incapable to provide a holistic understanding of the RF. In this paper, we propose a two-dimensional, easy-to-grasp visualization technique that follows a botanical approach and illustrates several key parameters necessary to understand why a given RF performs in a certain way. The method allows customized mappings of RF characteristics to visual properties and provides the possibility to interactively analyze the forest structure. This allows to determine trees that perform extraordinarily well or bad, to analyze the reasons for their performance, and thus to gain insights into how to change parameter setting to increase performance or efficiency.