# Sparse-group boosting -- Unbiased group and variable selection

arXiv: https://arxiv.org/abs/2206.06344 · HTML: https://mlsanity.com/paper/2206.06344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems](https://mlsanity.com/paper/1712.05910/) (2017)
- [Adaptive sparse group LASSO in quantile regression](https://mlsanity.com/paper/1911.01081/) (2019)
- [Variable Selection with Second-Generation P-Values](https://mlsanity.com/paper/2012.07941/) (2020)

## Built on by (most cited first, 3 of 3)

- [Factors other than climate change are currently more important in predicting how well fruit farms are doing financially](https://mlsanity.com/paper/2301.07685/) (2023)
- [Using interpretable boosting algorithms for modeling environmental and agricultural data](https://mlsanity.com/paper/2305.02699/) (2023)
- [Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation](https://mlsanity.com/paper/2405.21037/) (2024)

## Abstract (arXiv)

In the presence of grouped covariates, we propose a framework for boosting that allows to enforce sparsity within and between groups. By using component-wise and group-wise gradient boosting at the same time with adjusted degrees of freedom, a model with similar properties as the sparse group lasso can be fitted through boosting. We show that within-group and between-group sparsity can be controlled by a mixing parameter and discuss similarities and differences to the mixing parameter in the sparse group lasso. With simulations, gene data as well as agricultural data we show the effectiveness and predictive competitiveness of this estimator. The data and simulations suggest, that in the presence of grouped variables the use of sparse group boosting is associated with less biased variable selection and higher predictability compared to component-wise boosting. Additionally, we propose a way of reducing bias in component-wise boosting through the degrees of freedom.