Gradient boosting machines, a tutorial

作者：Alexey Natekin, Alois Knoll · 发表于：Frontiers in Neurorobotics · 年份：2013 · DOI：10.3389/fnbot.2013.00021 · 被引用次数：3933 · 研究领域：Machine Learning and ELM、Neural Networks and Applications、Face and Expression Recognition

Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods with a strong focus on machine learning aspects of modeling. A theoretical information is complemented with descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. Three practical examples of gradient boosting applications are presented and comprehensively analyzed.