The mission of the Symposium of University Research and Creative Expression (SOURCE) is to provide a university-wide forum for Central Washington University (CWU) students, encouraging equity, diversity, and inclusivity, representing all disciplines and experience levels, to present their mentored research, scholarship, and creative works in a juried environment that meets professional conference standards and expectations.
The 2022 SOURCE program is hybrid. Pre-recorded virtual talks are colored green and can be watched anytime. Live/in-person sessions with Zoom access can be found in the daily schedule. Thank you for joining us!
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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.
