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Found 5 papers, 2 papers with code
Calibrated Explanations for Regression
This paper introduces an extension of a feature importance explanation method, Calibrated Explanations (CE), previously only supporting classification, with support for standard regression and probabilistic regression, i. e., the probability that the target is above an arbitrary threshold.
Approximating Score-based Explanation Techniques Using Conformal Regression
The results also suggest that the proposed method can produce tight intervals, while providing validity guarantees.
Well-Calibrated Probabilistic Predictive Maintenance using Venn-Abers
In the experiments, we apply Venn-Abers calibration to decision trees, random forests and XGBoost models, showing how both overconfident and underconfident models are corrected.
Calibrated Explanations: with Uncertainty Information and Counterfactuals
While local explanations for AI models can offer insights into individual predictions, such as feature importance, they are plagued by issues like instability.
A Meta Survey of Quality Evaluation Criteria in Explanation Methods
In this paper, we have conducted a semi-systematic meta-survey over fifteen literature surveys covering the evaluation of explainability to identify existing criteria usable for comparative evaluations of explanation methods.
