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2010 JMLR JMLR 2010

Model-based Boosting 2.0

Abstract

We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. [abs] [ pdf ][ bib ] [ code ] © JMLR 2010. (edit, beta)

🧭 Keyword Pioneer — risk function
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Reinforcement Learning

Authors

Torsten Hothorn , Peter Bühlmann , Thomas Kneib , Matthias Schmid , Benjamin Hofner

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Regression Machine Learning > Optimization & Theory > Optimization

Keywords

additive model regression tree penalized least square risk function
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