https://github.com/jphall663/interpretable_machine_learning_with_python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
https://github.com/jphall663/interpretable_machine_learning_with_python

accountability data-mining data-science decision-tree fairness fatml gradient-boosting-machine h2o iml interpretability interpretable interpretable-ai interpretable-machine-learning interpretable-ml lime machine-learning machine-learning-interpretability python transparency xai

Last synced: 2 months ago
JSON representation

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

• Host: GitHub
• URL: https://github.com/jphall663/interpretable_machine_learning_with_python
• Owner: jphall663
• Created: 2018-03-14T00:02:53.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-06-17T22:44:43.000Z (7 months ago)
• Last Synced: 2024-08-03T23:25:50.283Z (6 months ago)
• Topics: accountability, data-mining, data-science, decision-tree, fairness, fatml, gradient-boosting-machine, h2o, iml, interpretability, interpretable, interpretable-ai, interpretable-machine-learning, interpretable-ml, lime, machine-learning, machine-learning-interpretability, python, transparency, xai
• Language: Jupyter Notebook
• Homepage:
• Size: 34.7 MB
• Stars: 669
• Watchers: 43
• Forks: 207
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-machine-learning-resources - **[Tutorial
• awesome-machine-learning-interpretability - Interpretable Machine Learning with Python