XGBoost 文档 — xgboost 3.1.0 文档 - XGBoost 文档
XGBoost 提供了一种并行树提升（也称为 GBDT，GBM）方法，可以快速准确地解决许多数据科学问题。 相同的代码可以在主要的分布式环境（Hadoop、SGE、MPI）上运行，并且可以解 …
GitHub - dmlc/xgboost: Scalable, Portable and Distributed …
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting …
Package 'xgboost' reference manual - cran.dev
When it comes to serializing XGBoost models, it's possible to use R serializers such as save () or saveRDS () to serialize an XGBoost model object, but XGBoost also provides its own …
XGBoost
Multiple Languages Supports multiple languages including C++, Python, R, Java, Scala, Julia.
XGBoost Parameters — xgboost 0.90 documentation
Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters. General parameters relate to which booster we are using to …
AiLake - v0.7.0-beta
XGBoost 中文文档 XGBoost是一个优化的分布式梯度增强库，旨在实现高效，灵活和便携。 它在 Gradient Boosting 框架下实现机器学习算法。 XGBoost提供并行树提升（也称 …
XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed …
XGBoost - Wikipedia
XGBoost[2] (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, [3] R, [4] Julia, [5] Perl, [6] …
XGBoost Documentation — xgboost 0.4 documentation
The goal of this library is to push the extreme of the computation limits of machines to provide a scalable, portable and accurate for large scale tree boosting. This document is hosted at …
XGBoost 文档 — xgboost 3.0.2 文档 - XGBoost 文档
XGBoost 提供了一种并行树增强（也称为 GBDT、GBM）方法，能够快速准确地解决许多数据科学问题。 相同的代码可以在主要分布式环境（Hadoop、SGE、MPI）上运行，并能解决超过 …