https://github.com/alexioannides/lime-interpretable-ml

An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.
https://github.com/alexioannides/lime-interpretable-ml

data-science interpretability lime machine-learning numpy pandas pydata python scikit-learn

Last synced: 2 months ago
JSON representation

An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.

• Host: GitHub
• URL: https://github.com/alexioannides/lime-interpretable-ml
• Owner: AlexIoannides
• Created: 2018-02-05T22:22:47.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-02-19T15:52:22.000Z (almost 6 years ago)
• Last Synced: 2023-02-28T18:18:53.243Z (almost 2 years ago)
• Topics: data-science, interpretability, lime, machine-learning, numpy, pandas, pydata, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 5.17 MB
• Stars: 10
• Watchers: 1
• Forks: 5
• Open Issues: 0

Awesome Lists containing this project