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.
Cite
Text
Hothorn et al. "Model-Based Boosting 2.0." Machine Learning Open Source Software, 2010.Markdown
[Hothorn et al. "Model-Based Boosting 2.0." Machine Learning Open Source Software, 2010.](https://mlanthology.org/mloss/2010/hothorn2010jmlr-modelbased/)BibTeX
@article{hothorn2010jmlr-modelbased,
title = {{Model-Based Boosting 2.0}},
author = {Hothorn, Torsten and Bühlmann, Peter and Kneib, Thomas and Schmid, Matthias and Hofner, Benjamin},
journal = {Machine Learning Open Source Software},
year = {2010},
pages = {2109-2113},
volume = {11},
url = {https://mlanthology.org/mloss/2010/hothorn2010jmlr-modelbased/}
}