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Commit 56096287 authored by galvesda's avatar galvesda
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# ExpOut # ExpOut
This project is an extension of LimeOut[1]. It aims at tackle process fairness for classification, while keeping the accuracy level (or improving). This project is an extension of LimeOut[1]. It aims at tackle process fairness for classification, while keeping the accuracy level (or improving).
More precisely, ExpOut incorporates different explainers. More precisely, ExpOut incorporates different explainers.
Classifiers available: Classifiers available:
* Multilayer Perceptron * Multilayer Perceptron
* Logistic Regression * Logistic Regression
* Random Forest * Random Forest
* Bagging * Bagging
* AdaBoost * AdaBoost
* Gaussian Mixture * Gaussian Mixture
* Gradiente Boosting * Gradiente Boosting
Explainers Explainers
* LIME * LIME
* Anchors * Anchors
# Example # Example
`runner --data german.data --exp anchors --trainsize 0.8 --algo mlp --cat_features 0 2 3 5 6 8 9 11 13 14 16 18 19 --drop 8 18 19` `python runner.py --data german.data --trainsize 0.8 --algo mlp --max_features 10 --cat_features 0 2 3 5 6 8 9 11 13 14 16 18 19 --drop 8 18 19 --exp anchors`
# References # References
[1] Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli. LimeOut: An Ensemble Approach To Improve Process Fairness. 2020. ⟨hal-02864059v2⟩ [1] Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli. LimeOut: An Ensemble Approach To Improve Process Fairness. 2020. ⟨hal-02864059v2⟩
## Dependencies ## Dependencies
* Python >= 3.7 * Python >= 3.7
* Scikit-learn >= 0.20.3 * Scikit-learn >= 0.20.3
* numpy 1.16.4 * numpy 1.16.4
* pandas 0.24.2 * pandas 0.24.2
* scipy 1.3.0 * scipy 1.3.0
* seaborn 0.9.0 * seaborn 0.9.0
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