xhqing/autogbm

Automated machine learning framework in tabular domain, a highly efficient automated gradient boosting machine.

autogbm

Example

pipenv run python exam.py

pipenv install --skip-lock --dev ipykernel
pipenv run python -m ipykernel install --name autogbm-bPW5dF2p

Description

Automated machine learning framework in tabular domain, a highly efficient automated gradient boosting machine.

Reference Papers

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "LightGBM: A Highly Efficient Gradient Boosting Decision Tree". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.

Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin "CatBoost: gradient boosting with categorical features support". Workshop on ML Systems at NIPS 2017.

Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016

License

Apache License 2.0