We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for ⬚tting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to ⬚t interpretable models of di⬚erent complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial.
%0 Report
%1 hofner_model-based_2012
%A Hofner, B.
%A Mayr, A.
%A Robinzonov, N.
%A Schmid, M.
%C Munich
%D 2012
%K Boosting, R, Tutorial
%N 120
%P 24
%T Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost
%U http://epub.ub.uni-muenchen.de/12754/
%X We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for ⬚tting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to ⬚t interpretable models of di⬚erent complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial.
@techreport{hofner_model-based_2012,
abstract = {We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for ⬚tting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to ⬚t interpretable models of di⬚erent complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial.},
added-at = {2017-01-09T13:57:26.000+0100},
address = {Munich},
author = {Hofner, B. and Mayr, A. and Robinzonov, N. and Schmid, M.},
biburl = {https://www.bibsonomy.org/bibtex/2fc7ff7db3c41a35c25b4943761c65e60/yourwelcome},
institution = {Department of Statistics, Ludwig-Maximilians-Universität},
interhash = {e9f6669c1a97b7511ad4aa5d037759f3},
intrahash = {fc7ff7db3c41a35c25b4943761c65e60},
keywords = {Boosting, R, Tutorial},
number = 120,
pages = 24,
shorttitle = {Model-based {Boosting} in {R}},
timestamp = {2017-01-09T14:01:11.000+0100},
title = {Model-based {Boosting} in {R}: {A} {Hands}-on {Tutorial} {Using} the {R} {Package} mboost},
type = {Technical {Report}},
url = {http://epub.ub.uni-muenchen.de/12754/},
urldate = {2012-08-24},
year = 2012
}