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Explainable AI for Trees: From Local Explanations to Global
  Understanding

Explainable AI for Trees: From Local Explanations to Global Understanding

11 May 2019
Scott M. Lundberg
G. Erion
Hugh Chen
A. DeGrave
J. Prutkin
B. Nair
R. Katz
J. Himmelfarb
N. Bansal
Su-In Lee
    FAtt
ArXiv (abs)PDFHTML

Papers citing "Explainable AI for Trees: From Local Explanations to Global Understanding"

41 / 41 papers shown
Title
RanDeS: Randomized Delta Superposition for Multi-Model Compression
RanDeS: Randomized Delta Superposition for Multi-Model Compression
Hangyu Zhou
Aaron Gokaslan
Volodymyr Kuleshov
Bharath Hariharan
MoMe
86
0
0
16 May 2025
Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management
Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management
Priyam Ganguly
Ramakrishna Garine
Isha Mukherjee
48
0
0
24 Feb 2025
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
Oladipo A. Madamidola
Felix Ngobigha
Adnane Ez-zizi
AAML
95
6
0
07 Jul 2024
Regression Trees Know Calculus
Regression Trees Know Calculus
Nathan Wycoff
85
0
0
22 May 2024
GLOBE-CE: A Translation-Based Approach for Global Counterfactual
  Explanations
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations
Dan Ley
Saumitra Mishra
Daniele Magazzeni
LRM
95
19
0
26 May 2023
Explaining black box text modules in natural language with language
  models
Explaining black box text modules in natural language with language models
Chandan Singh
Aliyah R. Hsu
Richard Antonello
Shailee Jain
Alexander G. Huth
Bin Yu
Jianfeng Gao
MILM
82
58
0
17 May 2023
Relational Local Explanations
Relational Local Explanations
V. Borisov
Gjergji Kasneci
FAtt
70
0
0
23 Dec 2022
Feature Selection with Distance Correlation
Feature Selection with Distance Correlation
Ranit Das
Gregor Kasieczka
David Shih
61
14
0
30 Nov 2022
MLC at HECKTOR 2022: The Effect and Importance of Training Data when
  Analyzing Cases of Head and Neck Tumors using Machine Learning
MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning
Vajira Thambawita
A. Storaas
Steven A. Hicks
Pål Halvorsen
Michael A. Riegler
89
1
0
30 Nov 2022
An interpretable imbalanced semi-supervised deep learning framework for
  improving differential diagnosis of skin diseases
An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases
Futian Weng
Yuanting Ma
J. Sun
Shijun Shan
Qiyuan Li
Jianping Zhu
Yang Wang
Yan Xu
108
0
0
20 Nov 2022
A $k$-additive Choquet integral-based approach to approximate the SHAP
  values for local interpretability in machine learning
A kkk-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning
G. D. Pelegrina
L. Duarte
M. Grabisch
FAttTDI
76
31
0
03 Nov 2022
Supply Chain Characteristics as Predictors of Cyber Risk: A
  Machine-Learning Assessment
Supply Chain Characteristics as Predictors of Cyber Risk: A Machine-Learning Assessment
Kevin Hu
R. Levi
Raphael Yahalom
El Ghali Zerhouni
19
1
0
27 Oct 2022
Explanation Method for Anomaly Detection on Mixed Numerical and
  Categorical Spaces
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces
Iñigo López-Riobóo Botana
Carlos Eiras-Franco
Julio César Hernández Castro
Amparo Alonso-Betanzos
122
0
0
09 Sep 2022
PDD-SHAP: Fast Approximations for Shapley Values using Functional
  Decomposition
PDD-SHAP: Fast Approximations for Shapley Values using Functional Decomposition
Arne Gevaert
Yvan Saeys
FAttTDI
45
2
0
26 Aug 2022
Predicting tacrolimus exposure in kidney transplanted patients using
  machine learning
Predicting tacrolimus exposure in kidney transplanted patients using machine learning
A. Storaas
A. Aasberg
Pål Halvorsen
Michael A. Riegler
Inga Strümke
30
1
0
09 May 2022
Explainable Machine Learning for Predicting Homicide Clearance in the
  United States
Explainable Machine Learning for Predicting Homicide Clearance in the United States
G. Campedelli
40
17
0
09 Mar 2022
Introducing explainable supervised machine learning into interactive
  feedback loops for statistical production system
Introducing explainable supervised machine learning into interactive feedback loops for statistical production system
Carlos Mougan
G. Kanellos
Johannes Micheler
Jose Martinez
Thomas Gottron
58
1
0
07 Feb 2022
Fairness Implications of Encoding Protected Categorical Attributes
Fairness Implications of Encoding Protected Categorical Attributes
Carlos Mougan
J. Álvarez
Salvatore Ruggieri
Steffen Staab
FaML
79
16
0
27 Jan 2022
Fighting Money Laundering with Statistics and Machine Learning
Fighting Money Laundering with Statistics and Machine Learning
R. Jensen
Alexandros Iosifidis
82
16
0
11 Jan 2022
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for
  Machine Learning
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Yongchan Kwon
James Zou
TDI
99
135
0
26 Oct 2021
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
Jilei Yang
FAtt
105
37
0
20 Sep 2021
Feature Synergy, Redundancy, and Independence in Global Model
  Explanations using SHAP Vector Decomposition
Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition
Jan Ittner
Lukasz Bolikowski
Konstantin Hemker
Ricardo Kennedy
FAtt
31
7
0
26 Jul 2021
Best of both worlds: local and global explanations with
  human-understandable concepts
Best of both worlds: local and global explanations with human-understandable concepts
Jessica Schrouff
Sebastien Baur
Shaobo Hou
Diana Mincu
Eric Loreaux
Ralph Blanes
James Wexler
Alan Karthikesalingam
Been Kim
FAtt
106
28
0
16 Jun 2021
Pitfalls of Explainable ML: An Industry Perspective
Pitfalls of Explainable ML: An Industry Perspective
Sahil Verma
Aditya Lahiri
John P. Dickerson
Su-In Lee
XAI
53
9
0
14 Jun 2021
Evaluating the Correctness of Explainable AI Algorithms for
  Classification
Evaluating the Correctness of Explainable AI Algorithms for Classification
Orcun Yalcin
Xiuyi Fan
Siyuan Liu
XAIFAtt
46
15
0
20 May 2021
Explaining a Series of Models by Propagating Shapley Values
Explaining a Series of Models by Propagating Shapley Values
Hugh Chen
Scott M. Lundberg
Su-In Lee
TDIFAtt
98
139
0
30 Apr 2021
What Makes a Scientific Paper be Accepted for Publication?
What Makes a Scientific Paper be Accepted for Publication?
Panagiotis Fytas
Georgios Rizos
Lucia Specia
38
10
0
14 Apr 2021
Randomization-based Machine Learning in Renewable Energy Prediction
  Problems: Critical Literature Review, New Results and Perspectives
Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives
Javier Del Ser
D. Casillas-Pérez
L. Cornejo-Bueno
Luis Prieto-Godino
J. Sanz-Justo
C. Casanova-Mateo
S. Salcedo-Sanz
AI4CE
70
44
0
26 Mar 2021
Shapley values for feature selection: The good, the bad, and the axioms
Shapley values for feature selection: The good, the bad, and the axioms
D. Fryer
Inga Strümke
Hien Nguyen
FAttTDI
105
206
0
22 Feb 2021
Activity Detection from Encrypted Remote Desktop Protocol Traffic
Activity Detection from Encrypted Remote Desktop Protocol Traffic
Lukasz Lapczyk
D. Skillicorn
21
1
0
06 Aug 2020
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A
  Top-Down Approach
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach
A. Moawad
E. Islam
Namdoo Kim
R. Vijayagopal
A. Rousseau
Wei Biao Wu
80
5
0
15 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
55
43
0
12 Jun 2020
Negation Detection for Clinical Text Mining in Russian
Negation Detection for Clinical Text Mining in Russian
Anastasia A. Funkner
Ksenia Balabaeva
Sergey Kovalchuk
11
8
0
10 Apr 2020
Causality-based Explanation of Classification Outcomes
Causality-based Explanation of Classification Outcomes
Leopoldo Bertossi
Jordan Li
Maximilian Schleich
Dan Suciu
Zografoula Vagena
XAICMLFAtt
255
46
0
15 Mar 2020
Fairness by Explicability and Adversarial SHAP Learning
Fairness by Explicability and Adversarial SHAP Learning
James M. Hickey
Pietro G. Di Stefano
V. Vasileiou
FAttFedML
129
19
0
11 Mar 2020
A Framework for Democratizing AI
A Framework for Democratizing AI
Shakkeel Ahmed
Ravi Mula
S. Dhavala
48
9
0
01 Jan 2020
Explainable artificial intelligence model to predict acute critical
  illness from electronic health records
Explainable artificial intelligence model to predict acute critical illness from electronic health records
S. Lauritsen
Mads Kristensen
Mathias Vassard Olsen
Morten Skaarup Larsen
K. M. Lauritsen
Marianne Johansson Jørgensen
Jeppe Lange
B. Thiesson
65
305
0
03 Dec 2019
Explaining Models by Propagating Shapley Values of Local Components
Explaining Models by Propagating Shapley Values of Local Components
Hugh Chen
Scott M. Lundberg
Su-In Lee
FAttFedML
74
110
0
27 Nov 2019
LionForests: Local Interpretation of Random Forests
LionForests: Local Interpretation of Random Forests
Ioannis Mollas
Nick Bassiliades
I. Vlahavas
Grigorios Tsoumakas
90
12
0
20 Nov 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail
  Forecasting
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Ana Lucic
H. Haned
Maarten de Rijke
68
64
0
17 Jul 2019
Improving performance of deep learning models with axiomatic attribution
  priors and expected gradients
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
G. Erion
Joseph D. Janizek
Pascal Sturmfels
Scott M. Lundberg
Su-In Lee
OODBDLFAtt
95
82
0
25 Jun 2019
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