期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 24, 期 1, 页码 174-183出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2017.2745158
关键词
Model selection; classification trees; visual parameter search; sensitivity analysis; Pareto optimality
资金
- COMET K1 program - Competence Centers for Excellent Technologies by BMVIT [854174]
- BMWFW
- Styria
- Styrian Business Promotion Agency - SFG
- Vienna Business Agency
- Austrian Funding Agency (FFG) within the scope of the K-project DEXHELPP [843550]
Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states. and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.
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