XGBoost

XGBoost (eXtreme Gradient Boosting) is an ensemble machine learning algorithm developed in 2016 by Tianqi Chen, Kailong Chen, and Carlos Guestrin. It is based on gradient boosting, an ensemble learning technique for increasing the accuracy of predictive models.

XGBoost is primarily used in supervised, machine learning applications, such as classification and regression. The algorithm works by taking datasets and combining weak learners to form strong predictive models. It sends each weak learner through a series of iterations, continuously learning from the mistakes and improving its accuracy.

XGBoost has become increasingly popular due to its superior performance and scalability compared to other machine learning algorithms. It has a swift implementation, and is known for its parallelism and ability to handle large datasets. It is also known for its great feature of allowing early stopping, which can be used to automatically select the best number of iterations.

XGBoost is widely adopted in industry, wherever accurate predictive models are needed. It has been shown to work well on a wide range of tasks including handwritten digit recognition, natural language processing, drug discovery, and computer vision. It is also commonly used in areas such as finance, economics, and health care.

XGBoost has become a popular choice among data scientists due to its accuracy, speed, and flexibility. It is rapidly gaining attention as one of the top predictive models for machine learning applications.