विधियों की तुलना करें

	व्याख्या योग्य गाऊसी प्रक्रिया	गॉसियन प्रक्रिया
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2006 (GP); 2017+ (XAI integration)	2006 (book); roots in Kriging, 1951)
प्रवर्तक	Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others	Rasmussen, C. E. & Williams, C. K. I.
प्रकार	Probabilistic model with post-hoc or built-in interpretability	Probabilistic non-parametric model
मौलिक स्रोत	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
उपनाम	XAI-GP, interpretable Gaussian process, explainable GP, transparent Gaussian process	GP, Gaussian Process Regression, GPR, Kriging
संबंधित	5	3
सारांश	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 Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.