# Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation

arXiv: https://arxiv.org/abs/2405.21037 · HTML: https://mlsanity.com/paper/2405.21037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 2 of 2)

- [Sparse-group boosting -- Unbiased group and variable selection](https://mlsanity.com/paper/2206.06344/) (2022)
- [Using interpretable boosting algorithms for modeling environmental and agricultural data](https://mlsanity.com/paper/2305.02699/) (2023)

## Abstract (arXiv)

This paper introduces a novel framework for reducing variable selection bias by balancing selection frequencies of base-learners in boosting and introduces the sgboost package in R, which implements this framework combined with sparse-group boosting. The group bias reduction algorithm employs a simulation-based approach to iteratively adjust the degrees of freedom for both individual and group base-learners, ensuring balanced selection probabilities and mitigating the tendency to over-select more complex groups. The efficacy of the group balancing algorithm is demonstrated through simulations. Sparse-group boosting offers a flexible approach for both group and individual variable selection, reducing overfitting and enhancing model interpretability for modeling high-dimensional data with natural groupings in covariates. The package uses regularization techniques based on the degrees of freedom of individual and group base-learners. Through comparisons with existing methods and demonstration of its unique functionalities, this paper provides a practical guide on utilizing sparse-group boosting in R, accompanied by code examples to facilitate its application in various research domains.