Explaining classifiers with measures of statistical association
From MaRDI portal
Publication:6168907
DOI10.1016/j.csda.2023.107701MaRDI QIDQ6168907
Elmar Plischke, Emanuele Borgonovo, Valentina Ghidini, Roman Hahn
Publication date: 11 July 2023
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
- Measuring and testing dependence by correlation of distances
- Ball Covariance: A Generic Measure of Dependence in Banach Space
- A fast algorithm for computing distance correlation
- Controlling the false discovery rate via knockoffs
- Brownian distance covariance
- Making best use of model evaluations to compute sensitivity indices
- Statistics in the big data era: failures of the machine
- Mutual information for explainable deep learning of multiscale systems
- Global sensitivity measures from given data
- Measuring association with Wasserstein distances
- Statistical inference for Sobol pick-freeze Monte Carlo method
- Sensitivity Analysis Based on Cramér--von Mises Distance
- On measures of dependence
- Measurements of separation among probability densities or random variables
- Capturing the Intangible Concept of Information
- Properties of Squeezed Binomial States and Squeezed Negative Binomial States
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
- Definitions, methods, and applications in interpretable machine learning
- Global sensitivity analysis with dependence measures
- Algorithmic Learning Theory
- Transformations and Invariance in the Sensitivity Analysis of Computer Experiments
- RELATIONS BETWEEN TWO SETS OF VARIATES
- Random forests
This page was built for publication: Explaining classifiers with measures of statistical association