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. 1905.07631
  4. Cited By
Disentangled Attribution Curves for Interpreting Random Forests and
  Boosted Trees

Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees

18 May 2019
Summer Devlin
Chandan Singh
W. James Murdoch
Bin Yu
    FAtt
ArXivPDFHTML

Papers citing "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

4 / 4 papers shown
Title
The impact of feature importance methods on the interpretation of defect
  classifiers
The impact of feature importance methods on the interpretation of defect classifiers
Gopi Krishnan Rajbahadur
Shaowei Wang
Yasutaka Kamei
Ahmed E. Hassan
FAtt
19
79
0
04 Feb 2022
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest
  Feature Importance
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance
Mattia Carletti
M. Terzi
Gian Antonio Susto
36
42
0
21 Jul 2020
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-Xia Yu
XAI
HAI
47
1,416
0
14 Jan 2019
A Survey on Deep Learning in Medical Image Analysis
A Survey on Deep Learning in Medical Image Analysis
G. Litjens
Thijs Kooi
B. Bejnordi
A. Setio
F. Ciompi
Mohsen Ghafoorian
Jeroen van der Laak
Bram van Ginneken
C. I. Sánchez
OOD
304
10,618
0
19 Feb 2017
1