A distributed gradient boosting library for solving tabular data science problems. It provides parallel tree boosting and runs the same code across single-machine and documented distributed environments.
Project overview
A distributed gradient boosting library for solving tabular data science problems. It provides parallel tree boosting and runs the same code across single-machine and documented distributed environments.
Project type
Data Processing
Use cases
Data Analysis
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Data teams, developers, and researchers working with tabular data who require parallel tree boosting across single-machine and distributed setups.
Key capabilities
Implements machine learning algorithms under the Gradient Boosting framework and provides parallel tree boosting for data science problems.
Executes the same code across documented distributed environments including Kubernetes, Hadoop, SGE, Dask, Spark, and PySpark.
Limitations and risks
The supplied sources identify package ecosystem availability but do not document installation or first-use steps.
Operating system, GPU, and minimum hardware requirements are not documented.
Getting started
The project supports local execution as a Python library, but the documentation does not specify installation or first-use steps.
Evidence and sources
README: XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***. It implements machine learning algorithms u…
GitHub project description: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and…