Model-based Boosting 2.0
2010 | journal article
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Documents & Media
Details
- Authors
- Hothorn, Torsten; Bühlmann, Peter; Kneib, Thomas ; Schmid, Matthias; Hofner, Benjamin
- Abstract
- We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
- Issue Date
- 2010
- Journal
- Journal of Machine Learning Reseach - Machine Learning Open Source Software
- Language
- English
