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| Erklärbarer Gauß-Prozess× | Gauß-Prozess× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2006 (GP); 2017+ (XAI integration) | 2006 (book); roots in Kriging, 1951) |
| 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 non-parametric model |
| 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 | GP, Gaussian Process Regression, GPR, Kriging |
| Verwandt≠ | 5 | 3 |
| 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 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. |
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