Model-based boosting in R: a hands-on tutorial using the R package mboost View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-02

AUTHORS

Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov, Matthias Schmid

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 fitting 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 fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial. More... »

PAGES

3-35

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00180-012-0382-5

DOI

http://dx.doi.org/10.1007/s00180-012-0382-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001684018


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