Interpretability of Machine Learning algorithms, through InterpretML.

This notebook is mainly based on the following paper "InterpretML: A Unified Framework for Machine Learning Interpretability" (H. Nori, S. Jenkins, P. Koch, and R. Caruana 2019).

Some ressources about InterpretML:

• Paper PDF link
• GitHub interpretml/interpret
• Website interpret.ml

This notebook was done by Ludovic Sterlin as the evaluation part of Supervized and Deep Learning of SDD-Algorithms in Machine Learning.

It go through InterpretMl framework with basic and more concrete example datasets using InterpretML Dashboard and EBM methods.

Notebook

• Notebook on GitHub

The notebbok was meant to be used with jupyter notebook.

Warning the general Dashboard from InterpretML, is not working on Colab until today (2021-01-11)!!! So you can use colab if you prefer but you will not be able to enjoy all the features of the Dashboard.

• Open on Colab