yandex-research/rtdl-revisiting-models
(NeurIPS 2021) Revisiting Deep Learning Models for Tabular Data
This project helps data scientists and machine learning practitioners evaluate and choose the best deep learning models for problems involving structured, tabular data. It provides tools to input your datasets and test how different neural network architectures perform, giving you insights into which models are strong baselines and which offer state-of-the-art performance for your specific data challenges. The primary users are data scientists who work with machine learning on real-world datasets.
324 stars. No commits in the last 6 months.
Use this if you are a data scientist looking to apply advanced deep learning techniques to tabular data problems and want to understand which models (like MLPs, ResNets, or FT-Transformers) offer the best performance and when.
Not ideal if you are exclusively working with gradient-boosted decision trees (GBDTs) and are not interested in exploring deep learning alternatives for tabular data.
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324
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58
Language
Python
License
Apache-2.0
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Last pushed
Nov 12, 2024
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