Understanding the decisions of tree-based ensembles and their relationships
is pivotal for machine learning model interpretation. Recent attempts to
mitigate the human-in-the-loop interpretation challenge have explored the
extraction of the decision structure underlying the model taking advantage of
graph simplification and path emphasis. However, while these efforts enhance
the visualisation experience, they may either result in a visually complex
representation or compromise the interpretability of the original ensemble
model. In addressing this challenge, especially in complex scenarios, we
introduce the Decision Predicate Graph (DPG) as a model-agnostic tool to
provide a global interpretation of the model. DPG is a graph structure that
captures the tree-based ensemble model and learned dataset details, preserving
the relations among features, logical decisions, and predictions towards
emphasising insightful points. Leveraging well-known graph theory concepts,
such as the notions of centrality and community, DPG offers additional
quantitative insights into the model, complementing visualisation techniques,
expanding the problem space descriptions, and offering diverse possibilities
for extensions. Empirical experiments demonstrate the potential of DPG in
addressing traditional benchmarks and complex classification scenarios.

通过决策谓词图（DPG）作为模型无关工具来全局解释基于树的集成模型，并提供额外的定量洞察力，以增强视觉技术，扩展问题空间描述，并提供各种扩展可能性。

决策谓词图：提升树集成的可解释性

Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles

Counterfactual explanations inform ways to achieve a desired outcome from a
machine learning model. However, such explanations are not robust to certain
real-world changes in the underlying model (e.g., retraining the model,
changing hyperparameters, etc.), questioning their reliability in several
applications, e.g., credit lending. In this work, we propose a novel strategy
-- that we call RobX -- to generate robust counterfactuals for tree-based
ensembles, e.g., XGBoost. Tree-based ensembles pose additional challenges in
robust counterfactual generation, e.g., they have a non-smooth and
non-differentiable objective function, and they can change a lot in the
parameter space under retraining on very similar data. We first introduce a
novel metric -- that we call Counterfactual Stability -- that attempts to
quantify how robust a counterfactual is going to be to model changes under
retraining, and comes with desirable theoretical properties. Our proposed
strategy RobX works with any counterfactual generation method (base method) and
searches for robust counterfactuals by iteratively refining the counterfactual
generated by the base method using our metric Counterfactual Stability. We
compare the performance of RobX with popular counterfactual generation methods
(for tree-based ensembles) across benchmark datasets. The results demonstrate
that our strategy generates counterfactuals that are significantly more robust
(nearly 100% validity after actual model changes) and also realistic (in terms
of local outlier factor) over existing state-of-the-art methods.

本文利用 RobX 策略生成树模型集合的鲁棒性反事实解释，并使用 Counterfactual Stability 度量方法评估这些解释的稳健性。结果表明，与现有的最先进的方法相比，我们的策略生成的反事实解释显著更加鲁棒和真实。