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Molnar, Christoph
258  Ergebnisse:
Personensuche X
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1

Marginal effects for non-linear prediction functions:

Scholbeck, Christian A. ; Casalicchio, Giuseppe ; Molnar, Christoph..
Data Mining and Knowledge Discovery.  , 2024
Link: https://doi.org/10.1007/..
 
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2

Relating the Partial Dependence Plot and Permutation Featur..:

, In: Communications in Computer and Information Science; Explainable Artificial Intelligence,
Molnar, Christoph ; Freiesleben, Timo ; König, Gunnar... - p. 456-479 , 2023
Link: https://doi.org/10.1007/..
 
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3

Model-agnostic feature importance and effects with dependen..:

Molnar, Christoph ; König, Gunnar ; Bischl, Bernd.
Data Mining and Knowledge Discovery.  , 2023
Link: https://doi.org/10.1007/..
 
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4

Model-agnostic interpretable machine learning:

Molnar, Christoph , 2022
Link: https://nbn-resolving.de..
 
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5

Beyond prediction: methods for interpreting complex models ..:

Wadoux, Alexandre M.J-C. ; Molnar, Christoph
Geoderma.  422 (2022)  - p. 115953 , 2022
Link: https://doi.org/10.1016/..
 
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6

Explainable AI Methods - A Brief Overview:

, In: xxAI - Beyond Explainable AI; Lecture Notes in Computer Science,
Holzinger, Andreas ; Saranti, Anna ; Molnar, Christoph.. - p. 13-38 , 2022
Link: https://doi.org/10.1007/..
 
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7

General Pitfalls of Model-Agnostic Interpretation Methods f..:

, In: xxAI - Beyond Explainable AI; Lecture Notes in Computer Science,
Molnar, Christoph ; König, Gunnar ; Herbinger, Julia... - p. 39-68 , 2022
Link: https://doi.org/10.1007/..
 
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8

Sampling, Intervention, Prediction, Aggregation: A Generali..:

, In: Machine Learning and Knowledge Discovery in Databases; Communications in Computer and Information Science,
Scholbeck, Christian A. ; Molnar, Christoph ; Heumann, Christian.. - p. 205-216 , 2020
Link: https://doi.org/10.1007/..
 
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9

Interpretable Machine Learning – A Brief History, State-of-..:

, In: ECML PKDD 2020 Workshops; Communications in Computer and Information Science,
Molnar, Christoph ; Casalicchio, Giuseppe ; Bischl, Bernd - p. 417-431 , 2020
Link: https://doi.org/10.1007/..
 
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10

Quantifying Model Complexity via Functional Decomposition f..:

, In: Machine Learning and Knowledge Discovery in Databases; Communications in Computer and Information Science,
Molnar, Christoph ; Casalicchio, Giuseppe ; Bischl, Bernd - p. 193-204 , 2020
Link: https://doi.org/10.1007/..
 
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11

Multi-Objective Counterfactual Explanations:

, In: Parallel Problem Solving from Nature – PPSN XVI; Lecture Notes in Computer Science,
Dandl, Susanne ; Molnar, Christoph ; Binder, Martin. - p. 448-469 , 2020
Link: https://doi.org/10.1007/..
 
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12

Visualizing the Feature Importance for Black Box Models:

, In: Machine Learning and Knowledge Discovery in Databases; Lecture Notes in Computer Science,
Casalicchio, Giuseppe ; Molnar, Christoph ; Bischl, Bernd - p. 655-670 , 2019
Link: https://doi.org/10.1007/..
 
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13

iml: An R package for Interpretable Machine Learning:

Molnar, Christoph
Journal of Open Source Software.  3 (2018)  26 - p. 786 , 2018
Link: https://doi.org/10.21105..
 
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14

Ökologische Inferenz:

, In: Exit Polls und Hybrid-Modelle,
Klima, André ; Thurner, Paul W. ; Molnar, Christoph.. - p. 127-154 , 2017
Link: https://doi.org/10.1007/..
 
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15

Estimation of voter transitions based on ecological inferen..:

Klima, André ; Thurner, Paul W. ; Molnar, Christoph..
AStA Advances in Statistical Analysis.  100 (2015)  2 - p. 133-159 , 2015
Link: https://doi.org/10.1007/..
 
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