Search Results for author:
Found 9 papers, 7 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.
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values. They quantify each parameter's contribution to BO's acquisition function.
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation
Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation.
Decomposing Global Feature Effects Based on Feature Interactions
We formally introduce generalized additive decomposition of global effects (GADGET), which is a new framework based on recursive partitioning to find interpretable regions in the feature space such that the interaction-related heterogeneity of local feature effects is minimized.
REPID: Regional Effect Plots with implicit Interaction Detection
Machine learning models can automatically learn complex relationships, such as non-linear and interaction effects.
Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models.
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models.
Grouped Feature Importance and Combined Features Effect Plot
However, for researchers and practitioners, it is often equally important to quantify the importance or visualize the effect of feature groups.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly.
