Machine learningMachine learning
可解释随机森林
可解释随机森林（Explainable Random Forest, XRF）结合了 Breiman 的随机森林集成模型的预测能力与系统性的事后归因方法——主要是 SHAP 值和平均不纯度减少（mean-decrease-in-impurity）重要性——以使模型决策透明且可审计。它同时提供高准确性和人类可解释的特征贡献，满足了监管机构、领域专家和学术评审员的需求。
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来源
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗
- Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI: 10.1023/A:1010933404324 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Random Forest (Interpretable Ensemble with Feature Attribution). ScholarGate. https://scholargate.app/zh/machine-learning/explainable-random-forest
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