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XGBoost

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About

XGBoost is an optimized distributed gradient boosting library designed for efficiency, flexibility, and portability. It implements machine learning algorithms under the Gradient Boosting framework, providing parallel tree boosting to solve data science problems quickly and accurately. It runs on major distributed environments (Hadoop, SGE, MPI) and handles datasets with billions of examples. It supports regression, classification, ranking, and user-defined objectives and is compatible with various languages (C++, Python, R, Java, Scala, Julia) and cloud platforms.

Platform

Keywords

machine learning gradient boosting parallel computing gbm gbdt

Task

machine learning

Features

• high performance

• scalable

• portable

• supports distributed training

• distributed on cloud (aws, gce, azure, yarn clusters)

• battle-tested

• supports multiple languages (c++, python, r, java, scala, julia)

• flexible

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