So sánh phương pháp

	Quy trình Gaussian Giải thích được	Rừng ngẫu nhiên có thể giải thích
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006 (GP); 2017+ (XAI integration)	2001–2017
Người khởi xướng	Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
Loại	Probabilistic model with post-hoc or built-in interpretability	Interpretable ensemble (bagging + post-hoc attribution)
Công trình gốc	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, 4765–4774.
Tên gọi khác	XAI-GP, interpretable Gaussian process, explainable GP, transparent Gaussian process	XRF, interpretable random forest, transparent random forest, random forest with explainability
Liên quan	5	4
Tóm tắt	An Explainable Gaussian Process (XAI-GP) combines the probabilistic, uncertainty-aware predictions of a Gaussian Process model with systematic interpretability tools — such as SHAP values, kernel decomposition, or sensitivity analysis — so that every prediction comes with both a calibrated confidence interval and an auditable explanation of which inputs drove it.	Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.