方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 可解释 K-近邻算法× | LIME：局部可解释模型无关解释× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1967 (KNN); 2010s (explainability extensions) | 2016 |
| 提出者≠ | Cover, T. & Hart, P. (KNN); XAI extensions by various authors | Marco Ribeiro, Sameer Singh & Carlos Guestrin |
| 类型≠ | Instance-based learning with explainability layer | post-hoc local explanation |
| 开创性文献≠ | Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144. DOI ↗ |
| 别名 | XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors | Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar |
| 相关≠ | 4 | 2 |
| 摘要≠ | Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers. | LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors. |
| ScholarGate数据集 ↗ |
|
|