Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations-Reference-Cited by-同舟云学术

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations

Published:2020 Issue: Volume: Page:205-216
ISSN:1865-0929
Container-title:Machine Learning and Knowledge Discovery in Databases
language:
Short-container-title:

Author:

Scholbeck Christian A.,Molnar Christoph,Heumann Christian,Bischl Bernd,Casalicchio Giuseppe

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-43823-4_18

Reference20 articles.

1. Apley, D.W.: Visualizing the effects of predictor variables in black box supervised learning models. arXiv e-prints arXiv:1612.08468 , December 2016

2. Bartus, T.: Estimation of marginal effects using margeff. Stata J. 5(3), 309–329 (2005)

3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

4. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);G Casalicchio,2019

5. Cohen, S., Dror, G., Ruppin, E.: Feature selection via coalitional game theory. Neural Comput. 19(7), 1939–1961 (2007)

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena;Minds and Machines;2024-07-15

2. Marginal effects for non-linear prediction functions;Data Mining and Knowledge Discovery;2024-02-27

3. Potential Applications of Explainable Artificial Intelligence to Actuarial Problems;Mathematics;2024-02-21

4. Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes;iScience;2024-02

5. Evidence from interpretable machine learning to inform spatial management of Palau's tuna fisheries;Ecosphere;2024-02