Comparer des méthodes

	Processus Gaussien Explicable	Processus Gaussien Régularisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006 (GP); 2017+ (XAI integration)	2006 (canonical formulation); kernel regularization roots 1990s
Auteur d'origine	Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others	Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic model with post-hoc or built-in interpretability	Probabilistic kernel model with regularization
Source fondatrice	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	XAI-GP, interpretable Gaussian process, explainable GP, transparent Gaussian process	Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression
Apparentées	5	4
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.	A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself.