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Shapley variable importance clouds for interpretable machine learning

Shapley variable importance clouds for interpretable machine learning

6 October 2021
Yilin Ning
M. Ong
Bibhas Chakraborty
B. Goldstein
Daniel Ting
Roger Vaughan
Nan Liu
    FAtt
ArXiv (abs)PDFHTML

Papers citing "Shapley variable importance clouds for interpretable machine learning"

8 / 8 papers shown
SHAPoint: Task-Agnostic, Efficient, and Interpretable Point-Based Risk Scoring via Shapley Values
SHAPoint: Task-Agnostic, Efficient, and Interpretable Point-Based Risk Scoring via Shapley Values
Tomer D. Meirman
Bracha Shapira
Noa Dagan
Lior Rokach
114
0
0
28 Sep 2025
"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and ExplanationsACM Transactions on Knowledge Discovery from Data (TKDD), 2025
Gianlucca L. Zuin
Adriano Veloso
204
0
0
11 Sep 2025
Evaluating AI fairness in credit scoring with the BRIO tool
Evaluating AI fairness in credit scoring with the BRIO tool
Greta Coraglia
Francesco Genco
Pellegrino Piantadosi
Enrico Bagli
Pietro Giuffrida
Davide Posillipo
Giuseppe Primiero
190
11
0
05 Jun 2024
A Guide to Feature Importance Methods for Scientific Inference
A Guide to Feature Importance Methods for Scientific Inference
F. K. Ewald
Ludwig Bothmann
Marvin N. Wright
J. Herbinger
Giuseppe Casalicchio
Gunnar Konig
299
33
0
19 Apr 2024
Hypothesis Testing and Machine Learning: Interpreting Variable Effects
  in Deep Artificial Neural Networks using Cohen's f2
Hypothesis Testing and Machine Learning: Interpreting Variable Effects in Deep Artificial Neural Networks using Cohen's f2Applied Soft Computing (Appl. Soft Comput.), 2023
Wolfgang Messner
CML
223
18
0
02 Feb 2023
Shapley variable importance cloud for machine learning models
Shapley variable importance cloud for machine learning models
Yilin Ning
Mingxuan Liu
Nan Liu
FAttTDI
224
1
0
16 Dec 2022
A novel interpretable machine learning system to generate clinical risk
  scores: An application for predicting early mortality or unplanned
  readmission in a retrospective cohort study
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyAmerican Medical Informatics Association Annual Symposium (AMIA), 2022
Yilin Ning
Siqi Li
M. Ong
F. Xie
Bibhas Chakraborty
Daniel Ting
Nan Liu
FAtt
166
26
0
10 Jan 2022
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
2.7K
21,148
0
16 Feb 2016
1
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