Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance

1 May 2019

Papers citing "Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance"

9 / 59 papers shown

Title
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees Joseph Janssen Vincent Guan Elina Robeva 17 7 0 21 Apr 2022
Marginal Effects for Non-Linear Prediction Functions Christian A. Scholbeck Giuseppe Casalicchio Christoph Molnar Bernd Bischl C. Heumann FAtt 9 9 0 21 Jan 2022
Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles Thomas W. Campbell H. Roder R. Georgantas J. Roder FedML TDI FAtt 19 16 0 16 Dec 2021
Spatial machine-learning model diagnostics: a model-agnostic distance-based approach A. Brenning 18 16 0 13 Nov 2021
Transforming Feature Space to Interpret Machine Learning Models A. Brenning FAtt 34 9 0 09 Apr 2021
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA Clément Bénard Sébastien Da Veiga Erwan Scornet 37 49 0 26 Feb 2021
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance L. Mentch Siyu Zhou 17 14 0 07 Mar 2020
Unbiased variable importance for random forests Markus Loecher FAtt 41 53 0 04 Mar 2020
Understanding complex predictive models with Ghost Variables Pedro Delicado D. Peña FAtt 26 4 0 13 Dec 2019