XGBoost is an open-source software library for developing and training machine learning models. It stands for “eXtreme Gradient Boosting” and was developed by Tianqi Chen in 2016.
XGBoost is an efficient implementation of the gradient boosting framework, a popular machine learning technique that combines multiple weak learners to produce a prediction model. The library is designed to be an optimized distributed gradient boosting system, which means it can handle large-scale data sets and is easy to scale up.
XGBoost has become an incredibly popular tool among data scientists, as it can produce highly accurate models while being very fast and efficient. The library is implemented in several languages, including C++, Java, Python, R and Julia, and provides bindings for many widely used ML frameworks, including TensorFlow and Scikit-learn.
XGBoost is widely used in various application fields, such as search engines, computer vision, recommendation systems, natural language processing, predictive analytics and many more. It has been adopted by the leading tech giants such as Google, Facebook, Microsoft, Amazon and Apple.
XGBoost remains one of the most powerful tools for data scientists and machine learning engineers. With its easy-to-use API and powerful optimization techniques, XGBoost provides powerful prediction models for various machine learning tasks.
