TinyGBT
TinyGBT(Tiny Gradient Boosted Trees) is a 200 line gradient boosted trees implementation written in pure python.
Since this code is not for production, it is not optimized for speed and memory usage.
Experiment
- Data: LightGBM's regression example data
- TinyGBT is quite slower than LightGBM, but achieve almost same testset RMSE on similar parameter settings.
| - | LightGBM | TinyGBT |
|---|---|---|
| RMSE of TestSet | 0.45652 | 0.45934 |
Reproduce experiment
- TinyGBT
git clone https://github.com/lancifollia/tinygbt.git
cd tinygbt
python example.py
- LightGBM
- run this code
Features
- For now, Regression with L2 loss supported only.
References
- [1] T. Chen and C. Guestrin. XGBoost: A Scalable Tree Boosting System. 2016.
- [2] G. Ke et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. 2017.
License
MIT