https://github.com/jd-opensource/utboost
A powerful tree-based uplift modeling system.
https://github.com/jd-opensource/utboost
causal-inference descision-tree gbm gradient-boosting uplift-modeling
Last synced: about 1 month ago
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A powerful tree-based uplift modeling system.
- Host: GitHub
- URL: https://github.com/jd-opensource/utboost
- Owner: jd-opensource
- License: mit
- Created: 2023-07-28T06:43:04.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-01-23T07:27:35.000Z (over 1 year ago)
- Last Synced: 2025-04-12T23:07:20.968Z (about 1 month ago)
- Topics: causal-inference, descision-tree, gbm, gradient-boosting, uplift-modeling
- Language: C++
- Homepage:
- Size: 188 KB
- Stars: 30
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# UTBoost
UTBoost is a powerful uplift modeling library based on boosting framework over decision trees.
It can handle large-scale RCT (randomized controlled trial) datasets and demonstrates superior predictive performance.
## Documentations
- [Installation Instructions](./doc/install.md)
- [Applying Model](./doc/applying_models.md)
## Quick Start
See the **[tutorial notebook](./examples/tutorial.ipynb)** for details.
```python
# import approaches
from utboost import UTBClassifier, UTBRegressor
# define model (CausalGBM algorithm)
model = UTBClassifier(
ensemble_type='boosting',
criterion='gbm',
iterations=20,
max_depth=4
)
# fit model
model.fit(X=X_train, ti=ti_train, y=y_train)
# predict outcomes
preds = model.predict(X_test)
# predict uplift
uplift_preds = preds[:, 1] - preds[:, 0]
```
## File Locations
* `src/*` — C++ code that ultimately compiles into a library
* `include/` — C++ header files
* `python-package/` — python package
## License
This project is open-sourced under the MIT license. You can find the terms of the license [here](http://opensource.org/licenses/MIT).
## Reference Paper
Junjie Gao, Xiangyu Zheng, DongDong Wang, Zhixiang Huang, Bangqi Zheng, Kai Yang. "[UTBoost: A Tree-boosting based System for Uplift Modeling](https://arxiv.org/abs/2312.02573)".