Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models

TL;DR

This paper introduces SPC-DT, a novel visualization method using General Line Coordinates to enhance interpretability of decision trees, aiding domain experts in evaluating and refining models.

Contribution

The paper presents a new visualization technique for decision trees based on Shifted Paired Coordinates, expanding existing methods' capabilities for better interpretability.

Findings

Visualizes attribute relations and data flow in decision trees

Shows case distribution and split thresholds effectively

Demonstrates benefits through case studies with real datasets

Abstract

Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. These methods use a version of General Line Coordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. The new method expands and complements the capabilities of existing methods, to visualize DT models. It shows: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) how tight each split is to thresholds in the DT nodes, and (5) the density of cases in parts of the n-D space. This information is important for domain experts for evaluating and improving the DT models, including avoiding overgeneralization and overfitting of models, along with their performance. The benefits of the methods are demonstrated in the case studies, using three real datasets.