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This repository belongs to our paper "Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models". In the paper we benchmark and asses the assumed performance-interpretability trade-off.

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NicoHambauer/Model-Performance-vs-Interpretability

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Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models

Environment Setup

We provide a simple script to set up the Conda environment tailored to your operating system. To get started, ensure that Conda is installed on your system and that you have cloned this repository to your local machine.

Should you face any issues during the next setup, please ensure that you have the necessary permissions to execute the script. If needed, you can make the script executable by running:

chmod +x setup_environment.sh

To set up the Conda environment, execute the following command in the root directory of this project:

./setup_environment.sh

This command will detect your operating system and create a Conda environment with the necessary dependencies for your platform. For macOS users, this will set up an environment that is compatible with Apple Silicon. For Unix/Windows users, the script will include support for cudatoolkit if applicable.

Datasets

Dataset names are aliased in the code as follows and can be retrieved via the links below:

Classification

Dataset name Alias Repository Link
college college https://www.kaggle.com/datasets/saddamazyazy/go-to-college-dataset
water water https://kaggle.com/adityakadiwal/water-potability
stroke stroke https://kaggle.com/fedesoriano/stroke-prediction-dataset
churn telco https://kaggle.com/blastchar/telco-customer-churn
recidivism compas https://www.kaggle.com/datasets/danofer/compass
credit fico https://community.fico.com/s/explainable-machine-learning-challenge
income adult https://archive.ics.uci.edu/ml/datasets/adult
bank bank https://archive.ics.uci.edu/ml/datasets/Bank+Marketing
airline airline https://kaggle.com/teejmahal20/airline-passenger-satisfaction
weather weather https://www.kaggle.com/datasets/jsphyg/weather-dataset-rattle-package

Regression

Dataset name Alias Repository Link
car car https://archive.ics.uci.edu/ml/datasets/automobile
student student https://archive.ics.uci.edu/ml/datasets/Student+Performance
productivity productivity https://archive.ics.uci.edu/ml/datasets/Productivity+Prediction+of+Garment+Employees
insurance medical https://www.kaggle.com/datasets/mirichoi0218/insurance
crimes crimes https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime
farming crab https://www.kaggle.com/datasets/sidhus/crab-age-prediction
wine wine https://archive.ics.uci.edu/ml/datasets/wine+quality
bike bike https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset
house housing https://www.kaggle.com/datasets/camnugent/california-housing-prices
diamond diamond https://www.kaggle.com/datasets/nancyalaswad90/diamonds-prices

License

This project is operated under an MIT license. Every file must contain the REUSE-compliant license and copyright declaration:

About

This repository belongs to our paper "Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models". In the paper we benchmark and asses the assumed performance-interpretability trade-off.

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