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pro vyhledávání: '"Scholbeck, Christian A."'
Autor:
Scholbeck, Christian A., Moosbauer, Julia, Casalicchio, Giuseppe, Gupta, Hoshin, Bischl, Bernd, Heumann, Christian
We argue that interpretations of machine learning (ML) models or the model-building process can be seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engin
Externí odkaz:
http://arxiv.org/abs/2312.13234
Autor:
Löwe, Holger, Scholbeck, Christian A., Heumann, Christian, Bischl, Bernd, Casalicchio, Giuseppe
Forward marginal effects have recently been introduced as a versatile and effective model-agnostic interpretation method particularly suited for non-linear and non-parametric prediction models. They provide comprehensible model explanations of the fo
Externí odkaz:
http://arxiv.org/abs/2310.02008
A clustering outcome for high-dimensional data is typically interpreted via post-processing, involving dimension reduction and subsequent visualization. This destroys the meaning of the data and obfuscates interpretations. We propose algorithm-agnost
Externí odkaz:
http://arxiv.org/abs/2209.10578
Autor:
Scholbeck, Christian A., Casalicchio, Giuseppe, Molnar, Christoph, Bischl, Bernd, Heumann, Christian
Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models and especially generalized linear models, the estimated coefficients cannot be interpreted as a direct feature
Externí odkaz:
http://arxiv.org/abs/2201.08837
Autor:
Molnar, Christoph, König, Gunnar, Herbinger, Julia, Freiesleben, Timo, Dandl, Susanne, Scholbeck, Christian A., Casalicchio, Giuseppe, Grosse-Wentrup, Moritz, Bischl, Bernd
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead t
Externí odkaz:
http://arxiv.org/abs/2007.04131
Autor:
Scholbeck, Christian A., Molnar, Christoph, Heumann, Christian, Bischl, Bernd, Casalicchio, Giuseppe
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present t
Externí odkaz:
http://arxiv.org/abs/1904.03959
Autor:
Scholbeck, Christian A., Casalicchio, Giuseppe, Molnar, Christoph, Bischl, Bernd, Heumann, Christian
Publikováno v:
Data Mining & Knowledge Discovery; Sep2024, Vol. 38 Issue 5, p2997-3042, 46p