XGBoost

What is XGBoost?

XGBoost is a scalable and flexible gradient boosting library that efficiently and accurately solves various data science problems. It implements machine learning algorithms under the Gradient Boosting framework, providing a parallel tree boosting (also known as GBDT, GBM) solution. XGBoost is designed to be efficient, flexible, and portable, with support for multiple programming languages, including Python, R, Java, Scala, and C++. It can run on a single machine, as well as on distributed platforms like Hadoop, Spark, Flink, and DataFlow, making it a versatile tool for handling large-scale data processing and modeling tasks

Highlights

• Scalable and Flexible Gradient Boosting framework
• Efficient and accurate solution for diverse data science problems
• Supports parallel tree boosting (GBDT, GBM)
• Compatible with multiple programming languages and platforms
• Able to run on single machines as well as distributed environments