Сравнение методов

	Объяснимая гауссовская модель процесса	Регуляризованный Гауссовский Процесс
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2006 (GP); 2017+ (XAI integration)	2006 (canonical formulation); kernel regularization roots 1990s
Автор метода	Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others	Rasmussen, C. E. & Williams, C. K. I.
Тип	Probabilistic model with post-hoc or built-in interpretability	Probabilistic kernel model with regularization
Основополагающий источник	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
Другие названия	XAI-GP, interpretable Gaussian process, explainable GP, transparent Gaussian process	Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression
Связанные	5	4
Сводка	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.