TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees

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 s...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2018-01, Vol.24 (1), p.174-183
Main Authors: Muhlbacher, Thomas, Linhardt, Lorenz, Moller, Torsten, Piringer, Harald
Format: Article
Language:English
Subjects:
Buildings
classification trees
Data models
Decision trees
Feedback
Focusing
Mathematical models
Measurement
Model selection
Parameter sensitivity
Pareto optimality
Pareto optimum
Sampling
Sensitivity
sensitivity analysis
Tradeoffs
Vegetation
visual parameter search
Visualization
Workflow
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Summary: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.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2017.2745158