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Visualizing the Feature Importance for Black Box Models

Visualizing the Feature Importance for Black Box Models

Giuseppe Casalicchio
Christoph Molnar
B. Bischl
    FAtt
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Papers citing "Visualizing the Feature Importance for Black Box Models"

12 / 12 papers shown
Title
Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning
Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning
Lefei Zhang
Lijie Hu
Di Wang
LRM
135
2
0
17 Feb 2025
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
Evandro S. Ortigossa
Fábio F. Dias
Brian Barr
Claudio T. Silva
L. G. Nonato
FAtt
84
3
0
25 Apr 2024
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
Hué Sullivan
Hurlin Christophe
Pérignon Christophe
Saurin Sébastien
46
0
0
12 Dec 2022
Consistent Individualized Feature Attribution for Tree Ensembles
Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg
G. Erion
Su-In Lee
FAtt
TDI
52
1,379
0
12 Feb 2018
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
538
21,613
0
22 May 2017
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
Giuseppe Casalicchio
Jakob Bossek
Michel Lang
Dominik Kirchhoff
P. Kerschke
B. Hofner
H. Seibold
Joaquin Vanschoren
B. Bischl
VLM
LRM
40
55
0
05 Jan 2017
An unexpected unity among methods for interpreting model predictions
An unexpected unity among methods for interpreting model predictions
Scott M. Lundberg
Su-In Lee
FAtt
42
110
0
22 Nov 2016
Model-Agnostic Interpretability of Machine Learning
Model-Agnostic Interpretability of Machine Learning
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
64
836
0
16 Jun 2016
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
123
3,672
0
10 Jun 2016
Distribution-Free Predictive Inference For Regression
Distribution-Free Predictive Inference For Regression
Jing Lei
M. G'Sell
Alessandro Rinaldo
Robert Tibshirani
Larry A. Wasserman
198
832
0
14 Apr 2016
Domain-Adversarial Training of Neural Networks
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin
E. Ustinova
Hana Ajakan
Pascal Germain
Hugo Larochelle
François Laviolette
M. Marchand
Victor Lempitsky
GAN
OOD
343
9,418
0
28 May 2015
OpenML: networked science in machine learning
OpenML: networked science in machine learning
Joaquin Vanschoren
Jan N. van Rijn
B. Bischl
Luís Torgo
FedML
AI4CE
95
1,310
0
29 Jul 2014