As the complexity of machine learning (ML) models increases and the applications in different (and critical) domains grow, there is a strong demand for more interpretable and trustworthy ML. One straightforward and model-agnostic way to interpret complex ML models is to train surrogate models, such as rule sets and decision trees, that sufficiently approximate the original ones while being simpler and easier-to-explain. Yet, rule sets can become very lengthy, with many if-else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal, providing users with model interpretability. We tackle this by proposing DeforestVis, a visual analytics tool that offers user-friendly summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the adaptive boosting (AdaBoost) technique. Our solution helps users to explore the complexity vs fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analyzing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case-by-case investigations. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

翻译：暂无翻译