Search Results for author:
Found 5 papers, 3 papers with code
Effector: A Python package for regional explanations
Effector implements well-established global effect methods, assesses the heterogeneity of each method and, based on that, provides regional effects.
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Consequently, RAMs fit one component per subregion of each feature instead of one component per feature.
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
RHALE quantifies the heterogeneity by considering the standard deviation of the local effects and automatically determines an optimal variable-size bin-splitting.
An Extendable Python Implementation of Robust Optimisation Monte Carlo
Our implementation can be used in two ways.
DALE: Differential Accumulated Local Effects for efficient and accurate global explanations
In this paper, we propose a novel ALE approximation, called Differential Accumulated Local Effects (DALE), which can be used in cases where the ML model is differentiable and an auto-differentiable framework is accessible.
