ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2211.08943
  4. Cited By
Comparing Explanation Methods for Traditional Machine Learning Models
  Part 1: An Overview of Current Methods and Quantifying Their Disagreement

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
ArXiv (abs)PDFHTML

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
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
FAttXAI
145
0
0
11 Feb 2025
Grouped Feature Importance and Combined Features Effect Plot
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
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
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
AI4TSAI4CE
87
404
0
19 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and
  Goals of Human Trust in AI
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi
Ana Marasović
Tim Miller
Yoav Goldberg
310
444
0
15 Oct 2020
Tropical and Extratropical Cyclone Detection Using Deep Learning
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
A Simple and Effective Model-Based Variable Importance Measure
Brandon M. Greenwell
Bradley C. Boehmke
Andrew J. McCarthy
FAttTDI
45
230
0
12 May 2018
Explanation in Artificial Intelligence: Insights from the Social
  Sciences
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
XAI
252
4,281
0
22 Jun 2017
Model-Agnostic Interpretability of Machine Learning
Model-Agnostic Interpretability of Machine Learning
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
86
839
0
16 Jun 2016
Unachievable Region in Precision-Recall Space and Its Effect on
  Empirical Evaluation
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
1