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Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
v1v2v3v4 (latest)

Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring

12 December 2022
Hué Sullivan
Hurlin Christophe
Pérignon Christophe
Saurin Sébastien
ArXiv (abs)PDFHTML

Papers citing "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring"

13 / 13 papers shown
Title
On Learning and Testing of Counterfactual Fairness through Data
  Preprocessing
On Learning and Testing of Counterfactual Fairness through Data Preprocessing
Haoyu Chen
Wenbin Lu
R. Song
Pulak Ghosh
FaML
61
6
0
25 Feb 2022
Variable Selection via Thompson Sampling
Variable Selection via Thompson Sampling
Yi Liu
Veronika Rockova
39
15
0
01 Jul 2020
Efficient nonparametric statistical inference on population feature
  importance using Shapley values
Efficient nonparametric statistical inference on population feature importance using Shapley values
B. Williamson
Jean Feng
FAtt
47
72
0
16 Jun 2020
Generalized SHAP: Generating multiple types of explanations in machine
  learning
Generalized SHAP: Generating multiple types of explanations in machine learning
Dillon Bowen
L. Ungar
FAtt
28
42
0
12 Jun 2020
A general framework for inference on algorithm-agnostic variable
  importance
A general framework for inference on algorithm-agnostic variable importance
B. Williamson
P. Gilbert
N. Simon
M. Carone
FAttCML
38
66
0
07 Apr 2020
Problems with Shapley-value-based explanations as feature importance
  measures
Problems with Shapley-value-based explanations as feature importance measures
Indra Elizabeth Kumar
Suresh Venkatasubramanian
C. Scheidegger
Sorelle A. Friedler
TDIFAtt
88
366
0
25 Feb 2020
The many Shapley values for model explanation
The many Shapley values for model explanation
Mukund Sundararajan
A. Najmi
TDIFAtt
64
635
0
22 Aug 2019
Explaining individual predictions when features are dependent: More
  accurate approximations to Shapley values
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
K. Aas
Martin Jullum
Anders Løland
FAttTDI
69
624
0
25 Mar 2019
Interpretable machine learning: definitions, methods, and applications
Interpretable machine learning: definitions, methods, and applications
W. James Murdoch
Chandan Singh
Karl Kumbier
R. Abbasi-Asl
Bin Yu
XAIHAI
201
1,444
0
14 Jan 2019
Visualizing the Feature Importance for Black Box Models
Visualizing the Feature Importance for Black Box Models
Giuseppe Casalicchio
Christoph Molnar
B. Bischl
FAtt
38
183
0
18 Apr 2018
Consistent Individualized Feature Attribution for Tree Ensembles
Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg
G. Erion
Su-In Lee
FAttTDI
66
1,405
0
12 Feb 2018
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
22,018
0
22 May 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OODFAtt
193
6,018
0
04 Mar 2017
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