Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost

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Authors Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov, Matthias Schmid
Journal/Conference Name Computational Statistics
Paper Category
Paper 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.
Date of publication 2014
Code Programming Language R

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