As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such 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. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity versus fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analysing 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 analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

翻译：随着机器学习（ML）模型复杂度的提升及其在各类（关键）领域应用的扩展，对更具可解释性与可信赖性的ML需求日益迫切。一种直接且模型无关的解释方法是通过训练替代模型（如规则集和决策树）来充分逼近原始模型——这类模型更简洁且易于解释。然而，当精准模拟复杂ML模型时，规则集可能因充斥大量if-else语句而变得冗长，决策树深度也会迅速增加。在此类场景下，两种方法均可能偏离其核心目标——为用户提供模型可解释性。为此，我们提出DeforestVis，一种可视化分析工具，通过采用自适应提升（AdaBoost）技术生成的替代决策桩（单层决策树）来总结复杂ML模型的行为特征。DeforestVis通过增量生成决策桩辅助用户探索复杂度与保真度之间的权衡，利用加权决策桩构建基于属性的解释来论证决策逻辑，并分析规则覆盖对训练实例在不同决策桩间分配的影响。独立测试集使用户能够监测手动规则修改的有效性，并基于逐例分析形成假设。我们通过两个应用案例及与数据分析师、模型开发者的专家访谈，展示了DeforestVis的适用性与实用性。