XGBoost is a scalable, portable, and distributed gradient boosting library (a tree ensemble machine learning algorithm). Initially created by Tianqi Chen from Washington University, it has been enriched by a Python wrapper by Bing Xu and an R interface by Tong He (you can read the story behind XGBoost directly from its principal creator at http://homes.cs.washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html). XGBoost is available for Python, R, Java, Scala, Julia, and C++, and it can work on a single machine (leveraging multithreading) in both Hadoop and Spark clusters:
- Website: https://xgboost.readthedocs.io/en/latest/
- Version at the time of print: 0.80
- Download page: https://github.com/dmlc/xgboost ...
