Methoden vergleichen

	Erklärbarer Gauß-Prozess	Regulierte Gauß-Prozesse
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2006 (GP); 2017+ (XAI integration)	2006 (canonical formulation); kernel regularization roots 1990s
Urheber	Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others	Rasmussen, C. E. & Williams, C. K. I.
Typ	Probabilistic model with post-hoc or built-in interpretability	Probabilistic kernel model with regularization
Wegweisende Quelle	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
Aliasnamen	XAI-GP, interpretable Gaussian process, explainable GP, transparent Gaussian process	Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression
Verwandt	5	4
Zusammenfassung	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.