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Christian A. Scholbeck
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General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ...
xxAI - Beyond Explainable AI. xxAI 2020. Lecture Notes in Computer Science 13200, 2022
270*2022
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations
CA Scholbeck, C Molnar, C Heumann, B Bischl, G Casalicchio
Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019 …, 2020
462020
Marginal Effects for Non-Linear Prediction Functions
CA Scholbeck, G Casalicchio, C Molnar, B Bischl, C Heumann
Data Mining and Knowledge Discovery 38, 2997–3042, 2024
142024
Algorithm-Agnostic Feature Attributions for Clustering
CA Scholbeck, H Funk, G Casalicchio
Explainable Artificial Intelligence. xAI 2023. Communications in Computer …, 2023
5*2023
Correction: Marginal effects for non-linear prediction functions
CA Scholbeck, G Casalicchio, C Molnar, B Bischl, C Heumann
Data Mining and Knowledge Discovery 38 (6), 4234-4235, 2024
2024
Bridging gaps in interpretable machine learning
CA Scholbeck
lmu, 2024
2024
Bridging the Gap Between Machine Learning and Sensitivity Analysis
CA Scholbeck, J Moosbauer, G Casalicchio, H Gupta, B Bischl, ...
arXiv preprint arXiv:2312.13234, 2023
2023
fmeffects: An R Package for Forward Marginal Effects
H Löwe, CA Scholbeck, C Heumann, B Bischl, G Casalicchio
arXiv preprint arXiv:2310.02008, 2023
2023
Bridging gaps in interpretable machine learning: sensitivity analysis, marginal effects, and cluster explanations
CA Scholbeck
Dissertation, München, Ludwig-Maximilians-Universität, 2024, 0
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