- 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] and Scala
- XGBoost Explained: A Beginner’s Guide - Medium
XGBoost, or Extreme Gradient Boosting, represents a cutting-edge approach to machine learning that has garnered widespread acclaim for its exceptional performance in tackling classification and
- XGBoost - GeeksforGeeks
Traditional machine learning models like decision trees and random forests are easy to interpret but often struggle with accuracy on complex datasets XGBoost short form for eXtreme Gradient Boosting is an advanced machine learning algorithm designed for efficiency, speed and high performance
- 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 framework
- What is the XGBoost algorithm and how does it work?
XGBoost is a machine learning algorithm that belongs to the ensemble learning category, specifically the gradient boosting framework It utilizes decision trees as base learners and employs regularization techniques to enhance model generalization
- XGBoost - University of Washington
XGBoost is an optimized distributed gradient boosting system designed to be highly efficient, flexible and portable It implements machine learning algorithms under the Gradient Boosting framework
- XGBoost
Supports multiple languages including C++, Python, R, Java, Scala, Julia Wins many data science and machine learning challenges Used in production by multiple companies Supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters Can be integrated with Flink, Spark and other cloud dataflow systems
- XGBoost 2. 0 | XGBoosting
XGBoost 2 0 brings significant improvements to the external memory support, particularly for the hist tree method Although still an experimental feature, the performance has been greatly enhanced by replacing the old file IO logic with memory mapping
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