Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
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Updated
Jun 17, 2024 - Jupyter Notebook
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
H2O.ai Machine Learning Interpretability Resources
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Article for Special Edition of Information: Machine Learning with Python
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
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