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Explainable Gaussian Process

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.

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Explainable Gaussian Process Regression and Classification

Taksonomisk metoderegister · ml-model / machine-learning

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. · ISBN 978-0-262-18253-9
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. · URL

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