jyansir/tmlp

[KDD 2024] Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs

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Experimental

This project helps data scientists and machine learning engineers build more accurate and efficient predictive models using tabular data. It takes your structured dataset and outputs a highly optimized model for tasks like customer churn prediction or fraud detection. This is for professionals who need to combine the strengths of traditional tree-based models with deep learning techniques.

No commits in the last 6 months.

Use this if you need to build high-performing predictive models on structured, tabular datasets and want to leverage both gradient-boosted trees and deep neural networks.

Not ideal if your primary data is unstructured, such as images, audio, or free-form text, as this is designed specifically for tabular data.

predictive-modeling tabular-data machine-learning data-science model-optimization
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 7 / 25

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Stars

11

Forks

1

Language

Python

License

MIT

Last pushed

Mar 03, 2025

Commits (30d)

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