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https://github.com/Quantmetry/resources-intelligibility
Some resources for intelligibility analysis of machine learning models.
https://github.com/Quantmetry/resources-intelligibility
Last synced: 2 months ago
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Some resources for intelligibility analysis of machine learning models.
- Host: GitHub
- URL: https://github.com/Quantmetry/resources-intelligibility
- Owner: Quantmetry
- License: mit
- Created: 2018-11-20T16:35:36.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2018-11-22T11:05:34.000Z (about 6 years ago)
- Last Synced: 2024-08-02T02:07:45.848Z (6 months ago)
- Language: Jupyter Notebook
- Size: 687 KB
- Stars: 4
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome_quantmetry - Quantmetry intelligibility resources
README
# resources-intelligibility
Some resources for intelligibility analysis of machine learning models, mostly in French.
Notes on setting up the project
-------------------------------- with a version of *python3* installed (tested with python 3.6), make sure you have access to *pip*.
- with the below instructions, create a local virtual environnment and activate it
- install requirements.txt```
$ python3 -m venv .venv
$ source .venv/bin/activate
(.venv) $ pip install -r requirements.txt
```- Go to the *data/* folder and download (~0.5Mo) the required data with the link you can find in *data/howtogetdata.txt*. At the end of this step, you should have a *carInsurance_train.csv* file in the *data/* folder.
- Start a jupyter server.```
(.venv) $ jupyter notebook
```Features
--------In the *notebooks/* folder, you will find some demos of several intelligibility techniques:
- Partie1\_Construction\_Modèle.ipynb
- Partie2\_Analyse_sensibilité\_des\_prédictions.ipynb
- Partie3\_Décomposition\_en\_contributions.ipynb
- Partie4\_Décomposition\_en\_règles.ipynbYou should run *Partie1* first because it will write a pickle with data and model, used by other notebooks. Afterwards, notebooks are independant.
Credits
-------
This work has been done by Quantmetry R&D, 2018.