---
title: Importance of Hyperparameters in ML
url: https://www.ml-quant.com/papers/repec/cup-pscirm-v-12-y-2024-i-4-p-841-848-9/
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: RePEc:cup:pscirm:v:12:y:2024:i:4:p:841-848_9
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.cambridge.org%2Fcore%2Fproduct%2Fidentifier%2FS2049847023000614%2Ftype%2Fjournal_article%3Bh%3Drepec%3Acup%3Apscirm%3Av%3A12%3Ay%3A2024%3Ai%3A4%3Ap%3A841-848_9
featured: 2024-10-17
citations: unknown
topic: ML & AI Methods
---


# Importance of Hyperparameters in ML

A study reveals that only 20.31% of machine learning papers in political science journals report their hyperparameters and tuning methods, indicating a need for more transparency and robustness in machine learning models.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.cambridge.org%2Fcore%2Fproduct%2Fidentifier%2FS2049847023000614%2Ftype%2Fjournal_article%3Bh%3Drepec%3Acup%3Apscirm%3Av%3A12%3Ay%3A2024%3Ai%3A4%3Ap%3A841-848_9
- Identifier: RePEc:cup:pscirm:v:12:y:2024:i:4:p:841-848_9
- Released: 2024-10-17
- First featured: Quant Letter No. 70 (2024-10-17): https://www.ml-quant.com/issues/2024-10-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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