Deselection of Base-Learners for Statistical Boosting -- with an Application to Distributional Regression

2022/02/03 by Strömer, Annika, Staerk, Christian, Klein, Nadja +3 · 3 citations
#Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2202.01657

Abstract

We present a new procedure for enhanced variable selection for component-wise gradient boosting. Statistical boosting is a computational approach that emerged from machine learning, which allows to fit regression models in the presence of high-dimensional data. Furthermore, the algorithm can lead to data-driven variable selection. In practice, however, the final models typically tend to include too many variables in some situations. This occurs particularly for low-dimensional data (p

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