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Locally Interpretable Models and Effects based on Supervised
  Partitioning (LIME-SUP)

Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)

2 June 2018
Linwei Hu
Jie Chen
V. Nair
Agus Sudjianto
    FAtt
ArXivPDFHTML

Papers citing "Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)"

4 / 4 papers shown
Title
SurvLIME: A method for explaining machine learning survival models
SurvLIME: A method for explaining machine learning survival models
M. Kovalev
Lev V. Utkin
E. Kasimov
187
90
0
18 Mar 2020
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
555
21,613
0
22 May 2017
"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
FAtt
FaML
587
16,828
0
16 Feb 2016
A review on global sensitivity analysis methods
A review on global sensitivity analysis methods
Bertrand Iooss
Paul Lemaître
74
891
0
09 Apr 2014
1