most citedEnsemble-based Uncertainty Quantification: Bayesian versus Credal Inference

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cs.LG2025

Random Forest Calibration

Mohammad Hossein Shaker, Eyke Hüllermeier

The Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests th…

cs.LG2021★ 2 cited

Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inference

Mohammad Hossein Shaker, Eyke Hüllermeier

The idea to distinguish and quantify two important types of uncertainty, often referred to as aleatoric and epistemic, has received increasing attention in machine learning researc…