Explainable machine learning (2022, 3rd lecture): Global model-agnostic methods
Lecture series 'Explainable machine learning' (University of Bonn, winter term 2022)
Topics covered:
- What are global model-agnostic methods and why are they useful?
- Attribution methods and feature importance
- Partial dependence plots
- Permutation feature importance
- Variance feature importance
- Prototypes and criticism
- Maximum mean discrepancy and witness function
Slides: https://uni-bonn.sciebo.de/s/gRqOQHKLLG8EUsL
Lecturer: Ribana Roscher
Winter term 2022, University of Bonn
Видео Explainable machine learning (2022, 3rd lecture): Global model-agnostic methods канала Ribana Roscher
Topics covered:
- What are global model-agnostic methods and why are they useful?
- Attribution methods and feature importance
- Partial dependence plots
- Permutation feature importance
- Variance feature importance
- Prototypes and criticism
- Maximum mean discrepancy and witness function
Slides: https://uni-bonn.sciebo.de/s/gRqOQHKLLG8EUsL
Lecturer: Ribana Roscher
Winter term 2022, University of Bonn
Видео Explainable machine learning (2022, 3rd lecture): Global model-agnostic methods канала Ribana Roscher
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30 ноября 2022 г. 2:17:51
01:12:14
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