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Benchmarking-KAN

This repository contains the official implementation of "A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data" (under review). You can use this codebase to replicate our experiments about benchmarking KAN networks on some of the most used real-world tabular datasets.

👀 Overview

Kolmogorov-Arnold Networks (KAN) has recently been introduced and gained much attention. In this work, we propose a benchmarking of KAN over some of the most used real-world datasets from UCI Machine Learning repository. We used the implementation of efficient KAN available here.

⚡️ Quick start

  1. Clone this repository: git clone https://github.com/eleonorapoeta/benchmarking-KAN.git
  2. Install the required dependencies: pip install -r requirements.txt
  3. Run the main script: python main.py

💻 Reproduce the Experiments

To reproduce the experiments conducted in our study, follow these steps:

After following the Quick Start guide, you can run python main.py specifying the following arguments:

  • --model_name = (kan, mlp, all) depending on the model you want to test.
  • --dataset_name = one of the tested datasets from UCI or yours.
  • --num_epochs = epochs of training.

📝 License

This project is licensed under the terms of the MIT license. See the LICENSE file for details.

🤝 Contributing

Contributions, issues, and feature requests are welcome! See our Contributing Guide for more details.