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Visualizing the Feature Importance for Black Box Models
v1v2v3 (latest)

Visualizing the Feature Importance for Black Box Models

18 April 2018
Giuseppe Casalicchio
Christoph Molnar
B. Bischl
    FAtt
ArXiv (abs)PDFHTML

Papers citing "Visualizing the Feature Importance for Black Box Models"

12 / 62 papers shown
Title
Model-agnostic Feature Importance and Effects with Dependent Features --
  A Conditional Subgroup Approach
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach
Christoph Molnar
Gunnar Konig
B. Bischl
Giuseppe Casalicchio
90
84
0
08 Jun 2020
Explainable Artificial Intelligence: a Systematic Review
Explainable Artificial Intelligence: a Systematic Review
Giulia Vilone
Luca Longo
XAI
110
271
0
29 May 2020
Towards Interpretable ANNs: An Exact Transformation to Multi-Class
  Multivariate Decision Trees
Towards Interpretable ANNs: An Exact Transformation to Multi-Class Multivariate Decision Trees
Duy T. Nguyen
Kathryn E. Kasmarik
H. Abbass
39
9
0
10 Mar 2020
On Interpretability of Artificial Neural Networks: A Survey
On Interpretability of Artificial Neural Networks: A Survey
Fenglei Fan
Jinjun Xiong
Mengzhou Li
Ge Wang
AAMLAI4CE
94
318
0
08 Jan 2020
A Neural Approach to Discourse Relation Signal Detection
A Neural Approach to Discourse Relation Signal Detection
Amir Zeldes
Yang Liu
41
6
0
08 Jan 2020
How Much Can We See? A Note on Quantifying Explainability of Machine
  Learning Models
How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models
G. Szepannek
MILMFAtt
32
6
0
29 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies,
  Opportunities and Challenges toward Responsible AI
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
Siham Tabik
...
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
285
6,386
0
22 Oct 2019
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework
  for Model-Agnostic Interpretations
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
Christian A. Scholbeck
Christoph Molnar
C. Heumann
B. Bischl
Giuseppe Casalicchio
108
27
0
08 Apr 2019
Quantifying Model Complexity via Functional Decomposition for Better
  Post-Hoc Interpretability
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
FAtt
69
60
0
08 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models
VINE: Visualizing Statistical Interactions in Black Box Models
M. Britton
FAtt
63
22
0
01 Apr 2019
The Landscape of R Packages for Automated Exploratory Data Analysis
The Landscape of R Packages for Automated Exploratory Data Analysis
M. Staniak
P. Biecek
25
15
0
27 Mar 2019
Variable Importance Clouds: A Way to Explore Variable Importance for the
  Set of Good Models
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models
Jiayun Dong
Cynthia Rudin
FAtt
53
27
0
10 Jan 2019
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