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Model-based boosting in high dimensions.

Torsten Hothorn1, Peter Bühlmann

  • 1Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstrasse 6, D-91054 Erlangen, Germany. Torsten.Hothorn@R-project.org

Bioinformatics (Oxford, England)
|August 31, 2006
PubMed
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The mboost R package provides functional gradient descent algorithms for optimizing loss functions. It enables fitting generalized linear, additive, and interaction models to complex, high-dimensional data using various base learners.

Area of Science:

  • Statistical computing
  • Machine learning

Background:

  • The mboost R package is available under the General Public License (GPL) from the Comprehensive R Archive Network (CRAN).

Purpose of the Study:

  • To introduce and describe the mboost R package for statistical modeling.
  • To highlight its implementation of functional gradient descent algorithms.

Main Methods:

  • Utilizes componentwise least squares for optimization.
  • Supports parametric linear forms, smoothing splines, and regression trees as base learners.
  • Implements boosting algorithms for generalized linear, additive, and interaction models.

Main Results:

  • Enables fitting of complex models to high-dimensional data.
  • Provides a flexible framework for optimizing general loss functions.

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Conclusions:

  • The mboost package offers a powerful tool for advanced statistical modeling in R.
  • Facilitates the application of boosting techniques for various regression and interaction models.