Understanding the Forest: A Visualization Tool to Support Decision Tree Analysis

Decision Trees (DTs) are one of the most widely used supervised Machine Learning algorithms. The algorithm constructs binary tree data structures that partition the data into smaller segments according to different rules. Hence, DTs can be used as a learning process of finding the optimal rules to separate and classify all items of a dataset. Since the algorithm relies on a decision process similar to rule-based decisions, they are easily interpretable. However, DTs can be difficult to analyse when dealing with large datasets and/or with multiple trees, i.e. ensembles. To ease the analysis and validation of these models, we developed a visual tool which includes a set of visualizations that overview and give details of a set of trees. Our tool aims to provide different perspectives over the same data and provide further insights on how decisions are being made. In this article, we overview our design process, present the different visualization models and their iterative validation. We present a use case in the telecommunications domain. In concrete, we use the visual tool to help understand how a model based on DTs decides which is the best channel (i.e., phonecall, e-mail, SMS) to contact a client.