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Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

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Interpretable Machine Learning

A collection of code, notebooks, and resources for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Want to contribute your own examples/code/resources? Just make a pull request.

Setup

cd interpretable-ml
virtualenv -p python3.6 env
source env/bin/activate
pip install -r python/jupyter-notebooks/requirements.txt

** Note: if using Ubuntu, you may have to manually install gcc. Try the following 
1. sudo apt-get update
2. sudo apt-get install gcc
3. sudo apt-get install --reinstall build-essential

Contents

Further reading:

Resources