AUTHOR=Natekin Alexey , Knoll Alois 

TITLE=Gradient boosting machines, a tutorial

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 7 - 2013

YEAR=2013

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2013.00021

DOI=10.3389/fnbot.2013.00021

ISSN=1662-5218

ABSTRACT=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. A theoretical information is complemented with many descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. A set of practical examples of gradient boosting applications are presented and comprehensively analyzed.