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.
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