---
title: MSBoost: Model Selection for Gradient Boosting
url: https://www.ml-quant.com/papers/ssrn/4867120/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4867120
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4867120
featured: 2024-06-20
citations: unknown
topic: ML & AI Methods
---


# MSBoost: Model Selection for Gradient Boosting

Model Selection for Gradient Boosting: The paper introduces a new gradient boosting approach that trains multiple models on residual errors simultaneously, proving especially effective for small and noisy datasets.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4867120
- Identifier: SSRN 4867120
- Released: 2024-06-15
- First featured: Quant Letter No. 54 (2024-06-20): https://www.ml-quant.com/issues/2024-06-20/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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