Comparer des méthodes

	Processus Gaussien Explicable	Gradient Boosting Explicable
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006 (GP); 2017+ (XAI integration)	2017–2020
Auteur d'origine	Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)
Type	Probabilistic model with post-hoc or built-in interpretability	Ensemble + explainability layer
Source fondatrice	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.
Alias	XAI-GP, interpretable Gaussian process, explainable GP, transparent Gaussian process	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
Apparentées	5	6
Résumé	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 Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.