dmlc/xgboost

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 DataFlow

What it solves

XGBoost addresses the need for a fast, accurate, and scalable way to solve data science problems using gradient boosting. It provides a system that can handle massive datasets—potentially exceeding billions of examples—while remaining efficient and portable across different computing environments.

How it works

It implements machine learning algorithms within the Gradient Boosting framework, specifically providing parallel tree boosting (also known as GBDT or GBM). This approach allows the library to be distributed across various environments such as Kubernetes, Hadoop, SGE, Dask, Spark, and PySpark.

Who it’s for

It is designed for data scientists and machine learning engineers who need to train models on large-scale data and require high performance and flexibility in their deployment environments.

Highlights

  • Parallel tree boosting for fast and accurate results.
  • Distributed computing support for Kubernetes, Hadoop, SGE, Dask, Spark, and PySpark.
  • Capable of processing datasets with billions of examples.
  • Optimized for efficiency, flexibility, and portability.

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