用新方法整合多种解释，让表格数据的模型说明更可信。
WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets
- 设计WISCA方法，结合分类概率与归一化贡献值生成共识解释。
- 在六个合成数据集上验证，结果与最可靠单个方法一致。
- 适合需要高可信解释的科研和医疗等关键领域使用。
尽管机器学习模型常以预测准确率优先，但在科学和高风险领域，可解释性仍至关重要。然而，不同的可解释性算法常产生冲突的结果，凸显出达成共识以统一解释的必要性。本研究在六个具有已知真实情况的合成数据集上训练了六种机器学习模型，并采用多种模型无关的可解释性技术。通过现有方法和一种新方法WISCA（加权缩放共识归因）生成共识解释。WISCA在所有测试中均与最可靠的单一方法保持一致，证明了稳健共识策略对提升解释可靠性的重要价值。
原文摘要 · Abstract (English)
While predictive accuracy is often prioritized in machine learning (ML) models, interpretability remains essential in scientific and high-stakes domains. However, diverse interpretability algorithms frequently yield conflicting explanations, highlighting the need for consensus to harmonize results. In this study, six ML models were trained on six synthetic datasets with known ground truths, utilizing various model-agnostic interpretability techniques. Consensus explanations were generated using established methods and a novel approach: WISCA (Weighted Scaled Consensus Attributions), which integrates class probability and normalized attributions. WISCA consistently aligned with the most reliable individual method, underscoring the value of robust consensus strategies in improving explanation reliability.
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