The pioneering neural network surpassing extremely-tuned XGboost and Catboost on varied tabular datasets.
Python
69
18 commits
updated Jun 11, 2024
README
ExcelFormer
This repository will include the original implementation and experiment codes of ExcelFormer. TabFormer is a pioneering neural network can surpass extensively-tuned XGboost, Catboost, and most tuned previous deep learning approaches on most of tabular data prediction tasks, in the supervised learning manner. It can be a go-to choice on tabualr dataset prediction competitions (e.g., Kaggle).
Even without hyper-parameter tuning, TabFormer performs comparable to tuned models. After hyper-parameter tuning, TabFormer typically outperforms them.
The implementation of TabFormer in the original paper is bin/excel_former.py.
How to test your model
You can test your models by adding them to bin directory and bin/__init__.py. Keep the same API we used in other models, and write your own evaluation script (run_default_config_excel.py as a reference).
Datasets:
The datasets (96 small tabular datasets + 21 large tabular datasets) are available at: https://huggingface.co/datasets/jyansir/excelformer.
Future work
We will organize our previous works on tabular prediction into Tabular AI Research group for industrial use (e.g. further architecture optimization or acceleration / compilation). If you want to include our model as a baseline in your paper, please use the version in this repository rather than the industrial one in the group repository.
Similar repos
The pioneering neural network surpassing extremely-tuned XGboost and Catboost on varied tabular datasets.
Python
69
18 commits
updated Jun 11, 2024
README
ExcelFormer
This repository will include the original implementation and experiment codes of ExcelFormer. TabFormer is a pioneering neural network can surpass extensively-tuned XGboost, Catboost, and most tuned previous deep learning approaches on most of tabular data prediction tasks, in the supervised learning manner. It can be a go-to choice on tabualr dataset prediction competitions (e.g., Kaggle).
Even without hyper-parameter tuning, TabFormer performs comparable to tuned models. After hyper-parameter tuning, TabFormer typically outperforms them.
The implementation of TabFormer in the original paper is bin/excel_former.py.
How to test your model
You can test your models by adding them to bin directory and bin/__init__.py. Keep the same API we used in other models, and write your own evaluation script (run_default_config_excel.py as a reference).
Datasets:
The datasets (96 small tabular datasets + 21 large tabular datasets) are available at: https://huggingface.co/datasets/jyansir/excelformer.
Future work
We will organize our previous works on tabular prediction into Tabular AI Research group for industrial use (e.g. further architecture optimization or acceleration / compilation). If you want to include our model as a baseline in your paper, please use the version in this repository rather than the industrial one in the group repository.