TimberTrek

Curate decision trees that align with your knowledge and values!

📺 Demo Video for "TimberTrek: Exploring and Curating Trustworthy Decision Trees with Interactive Visualization"

Web Demo

For a live web demo, visit: https://poloclub.github.io/timbertrek

You can use the web demo to explore your own Rashomon Sets! You just need to choose the my own set tab below the tool and upload a JSON file containing all decision paths in your Rashomon Set.

Check out this example notebook to see how to generate this JSON file.

Notebook Demos

You can directly use TimberTrek in your favorite computational notebooks (e.g. Jupyter Notebook/Lab, Google Colab, and VS Code Notebook).

Check out three live notebook demos below.

Jupyter Lite	Binder	Google Colab

Install

To use TimberTrek in a notebook, you would need to install TimberTrek with pip:

pip install timbertrek

Development

Clone or download this repository:

git clone git@github.com:poloclub/timbertrek.git

Install the dependencies:

npm install

Then run TimberTrek:

npm run dev

Navigate to localhost:3000. You should see TimberTrek running in your browser :)

Credits

Led by Jay Wang, TimberTrek is a result of a collaboration between ML and visualization researchers from Georgia Tech, Duke University, Fujitsu Laboratories, and University of British Columbia. TimberTrek is created by Jay Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Polo Chau, Cynthia Rudin, and Margo Seltzer.

License

The software is available under the MIT License.

Contact

If you have any questions, feel free to open an issue or contact Jay Wang.