https://github.com/talperetz/awesome-gradient-boosting
A curated list of Gradient Boosting resources for Data Scientists
https://github.com/talperetz/awesome-gradient-boosting
List: awesome-gradient-boosting
Last synced: 3 months ago
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A curated list of Gradient Boosting resources for Data Scientists
- Host: GitHub
- URL: https://github.com/talperetz/awesome-gradient-boosting
- Owner: talperetz
- License: mit
- Created: 2019-01-18T13:04:38.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-01-18T13:10:09.000Z (about 7 years ago)
- Last Synced: 2025-11-03T21:04:05.515Z (5 months ago)
- Size: 2.93 KB
- Stars: 16
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-gradient-boosting-machines - Awesome Gradient Boosting - Resources for data scientists (Related Awesome Lists / Comparison Studies)
README
Awesome Gradient Boosting
==========================
A curated list of Gradient Boosting resources for Data Scientists [](https://github.com/sindresorhus/awesome)
List of content
1. [Intro](#intro)
2. [Implementations](#implementations)
3. [Parameter Tuning](#parameter-tuning)
4. [Posts](#posts)
5. [Talks](#talks)
6. [Notebooks](#notebooks)
7. [Datasets](#datasets)
# Intro
- [Wikipedia](https://en.wikipedia.org/wiki/Gradient_boosting) - Gradient Boosting
- [XGBoost](https://xgboost.readthedocs.io/en/latest/tutorials/model.html) - Introduction to Boosted Trees
- [Kaggle](http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/) - a kaggle master explains gradient boosting
# Implementations
- XGBoost
* [XGBoost Github](https://github.com/dmlc/xgboost)
* [XGBoost Documentation](https://xgboost.readthedocs.io/en/latest/)
* [XGBoost Paper](https://arxiv.org/abs/1603.02754) - XGBoost: A Scalable Tree Boosting System
- LightGBM
* [LightGBM Github](https://github.com/Microsoft/LightGBM)
* [LightGBM Documentation](https://lightgbm.readthedocs.io/en/latest/)
* [LightGBM Paper](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf) - LightGBM: A Highly Efficient Gradient Boosting
Decision Tree
- Catboost
* [Catboost Github](https://github.com/catboost/catboost)
* [Catboost Documentation](https://tech.yandex.com/catboost/doc/dg/concepts/about-docpage/)
* [Catboost Paper](https://arxiv.org/pdf/1706.09516.pdf) - CatBoost: unbiased boosting with categorical features
- Other
* [sklearn](https://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting) - Gradient Boosting Guide
* [H20](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gbm.html) - GBM
# Parameter Tuning
- [Hyperspace](https://github.com/talperetz/hyperspace/tree/master/GBDTs) - Hyperparameters Spaces for Optimization
- [XGBoost](https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/) - Complete Guide to Parameter Tuning in XGBoost (with codes in Python)
- [LightGBM](https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html) - parameters tuning guides for different scenarios
- [Catboost](https://tech.yandex.com/catboost/doc/dg/concepts/parameter-tuning-docpage/) - some tips on the possible parameter settings
# Posts
* [Gradient Boosting Explained](http://arogozhnikov.github.io/2016/06/24/gradient_boosting_explained.html) - by Brilliantly wrong thoughts on science and programming
* [Gradient Boosting From Scratch](https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d) - by ML Review
* [CatBoost vs. Light GBM vs. XGBoost](https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db) - by Alvira Swalin on Towards Data Science
* [Mastering The New Generation of Gradient Boosting](https://towardsdatascience.com/https-medium-com-talperetz24-mastering-the-new-generation-of-gradient-boosting-db04062a7ea2) - by Tal Peretz on Towards Data Science
# Talks
* [Catboost](https://www.youtube.com/watch?v=8o0e-r0B5xQ) - Anna Veronika Dorogush on pydata
* [Can one do better than XGBoost?](https://www.youtube.com/watch?v=5CWwwtEM2TA) - Mateusz Susik on pydata
# Notebooks
* [Kaggle XGBoost](https://www.kaggle.com/dansbecker/xgboost)
* [Catboost + LightGBM + XGBoost](https://gist.github.com/talperetz/6030f4e9997c249b09409dcf00e78f91) - comparison of the 3 implementations on categorical dataset.
# Datasets
* [Catboost Datasets](https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_datasets-docpage/)
## License
[](http://creativecommons.org/publicdomain/zero/1.0/)
To the extent possible under law, [Tal Peretz](https://github.com/igorbarinov/) has waived all copyright and related or neighboring rights to this work.