Machine learningMachine learning
可解释高斯过程
可解释高斯过程 (Explainable Gaussian Process, XAI-GP) 将高斯过程模型 (Gaussian Process, GP) 的概率性、考虑不确定性的预测能力，与系统性的可解释性工具（如 SHAP 值、核分解或敏感性分析）相结合，使得每次预测都附带校准置信区间和可审计的输入驱动因素解释。
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来源
- Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Gaussian Process Regression and Classification. ScholarGate. https://scholargate.app/zh/machine-learning/explainable-gaussian-process
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