Visualisation of Random Forest classification

Abstract

Decision Trees (DTs) stand out as a prevalent choice among supervised Machine Learning algorithms. These algorithms form binary structures, effectively dividing data into smaller segments based on distinct rules. Consequently, DTs serve as a learning mechanism to identify optimal rules for the separation and classification of all elements within a dataset. Due to their resemblance to rule-based decisions, DTs are easy to interpret. Additionally, their minimal need for data pre-processing and versatility in handling various data types make DTs highly practical and user-friendly across diverse domains. Nevertheless, when confronted with extensive datasets or ensembles involving multiple trees, such as Random Forests, its analysis can become challenging. To facilitate the examination and validation of these models, we have developed a visual tool that incorporates a range of visualisations providing both an overview and detailed insights into a set of DTs. Our tool is designed to offer diverse perspectives on the same data, enabling a deeper understanding of the decision-making process. This article outlines our design approach, introduces various visualisation models, and details the iterative validation process. We validate our methodology through a telecommunications use case, specifically employing the visual tool to decipher how a DT-based model determines the optimal communication channel (i.e. phone call, email, SMS) for a telecommunication operator to use when contacting a client.