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2211.08943
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Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement
16 November 2022
Montgomery Flora
Corey K. Potvin
A. McGovern
Shawn Handler
FAtt
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Papers citing
"Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement"
10 / 10 papers shown
Title
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models
Célia Wafa Ayad
Thomas Bonnier
Benjamin Bosch
Sonali Parbhoo
Jesse Read
FAtt
XAI
145
0
0
11 Feb 2025
Grouped Feature Importance and Combined Features Effect Plot
Quay Au
J. Herbinger
Clemens Stachl
B. Bischl
Giuseppe Casalicchio
FAtt
81
46
0
23 Apr 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset
Antonios Mamalakis
I. Ebert‐Uphoff
E. Barnes
OOD
63
76
0
18 Mar 2021
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
AI4TS
AI4CE
87
404
0
19 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi
Ana Marasović
Tim Miller
Yoav Goldberg
307
444
0
15 Oct 2020
Tropical and Extratropical Cyclone Detection Using Deep Learning
Christina Kumler-Bonfanti
J. Stewart
D. Hall
M. Govett
36
37
0
18 May 2020
A Simple and Effective Model-Based Variable Importance Measure
Brandon M. Greenwell
Bradley C. Boehmke
Andrew J. McCarthy
FAtt
TDI
45
230
0
12 May 2018
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
XAI
252
4,273
0
22 Jun 2017
Model-Agnostic Interpretability of Machine Learning
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
86
839
0
16 Jun 2016
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation
Kendrick Boyd
Vítor Santos Costa
Jesse Davis
David Page
UQCV
98
105
0
18 Jun 2012
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