jyansir/tmlp
[KDD 2024] Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs
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.
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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.
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Language
Python
License
MIT
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Last pushed
Mar 03, 2025
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