DownloadXGBoost is an optimized distributed gradient boosting library, designed to be scalable, flexible, portable and highly efficient. It supports regression, classification, ranking and user defined objectives, and runs on all major operating systems and cloud platforms.
XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems. XGBoost can be used for Python, Java, Scala, R, C++ and more. It can run on a single machine, Hadoop, Spark, Dask, Flink and most other distributed environments, and is capable of solving problems beyond billions of examples.
Features
- Flexible - with support for regression, classification, ranking and user defined objectives
- Portable - cross-platform including cloud platforms
- Supports multiple programming languages
- Overcomes many data science and machine learning challenges
- Supports distributed training on multiple machines
- Integrates with Flink, Spark and other cloud dataflow systems
- Well-optimized backend system
Project Samples
License
Apache License V2.0
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