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Visualization of Machine Learning (ML) models is an important part of the MLprocess to enhance the interpretability and prediction accuracy of the MLmodels. This paper proposes a new method SPC-DT to visualize the Decision Tree(DT) as interpretable models. These methods use a version of General LineCoordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point isvisualized in a set of shifted pairs of 2-D Cartesian coordinates as a directedgraph. The new method expands and complements the capabilities of existingmethods, 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 ofcases in parts of the n-D space. This information is important for domainexperts for evaluating and improving the DT models, including avoidingovergeneralization and overfitting of models, along with their performance. Thebenefits of the methods are demonstrated in the case studies, using three realdatasets.

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