compboost: Modular Framework for Component-Wise Boosting

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Authors Daniel Schalk, Janek Thomas, Bernd Bischl
Journal/Conference Name J. Open Source Software
Paper Category
Paper Abstract In high-dimensional prediction problems, especially in the p ≫ n situation, feature selection is an essential tool. A fundamental method for problems of this type is componentwise gradient boosting, which automatically selects from a pool of base learners – e.g. simple linear effects or component-wise smoothing splines (Schmid & Hothorn, 2008) – and produces a sparse additive statistical model. Boosting these kinds of models maintains interpretability and enables unbiased model selection in high-dimensional feature spaces (Hofner, Hothorn, Kneib, & Schmid, 2012).
Date of publication 2018
Code Programming Language R

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