dmlc/xgboost is a library for solving data science problems with parallel gradient-boosted tree models across single-machine and distributed environments. It provides an efficient, flexible, and portable distributed gradient boosting implementation that can solve problems beyond billions of examples.
Project overview
The project provides portability by allowing the same code to run across documented distributed environments including Kubernetes, Hadoop, SGE, Dask, Spark, and PySpark.
Project type
Data Processing
Use cases
Data Analysis
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Data teams, developers, and researchers solving data science problems using tabular data with parallel gradient-boosted tree models.
Teams that need to execute the same model code across both single-machine and distributed environments.
Key capabilities
Implements machine learning algorithms under the Gradient Boosting framework and provides parallel tree boosting, also known as GBDT or GBM, for data science problems.
Runs the same code across documented distributed environments including Kubernetes, Hadoop, SGE, Dask, Spark, and PySpark.
Limitations and risks
Installation difficulty is unclear, and the sources identify language and package availability but do not document installation or first-use steps.
OS, GPU, and minimum hardware requirements are not documented in the supplied facts.
Getting started
The installation difficulty is unclear because the supplied sources identify language and package ecosystem availability but do not document installation or first-use steps. There is no documented first success path.
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…