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Purpose

This repository contains trained machine learning models generated as part of different scientific studies. The goal is to support reproducibility, transparency, and external validation of published research by making the final models openly accessible.

Each study has its own folder containing one or more trained models. Models are GDPR-compliant and do not include raw, sensitive or personal data.

Each study has its own folder containing one or more trained models.
No raw or sensitive data is included.

This repository contains only the trained models from the studies.


GDPR and Privacy Compliance

This repository is fully GDPR-compliant.

  • No personal data, raw datasets, or identifiable information are stored in this repository.
  • The machine learning models included here do not store or embed the training data used to create them.
  • Only the model parameters (e.g., coefficients, split rules, tree structures) are saved.
  • Any elements of model objects that could contain data (e.g., model frames) were removed before upload, where applicable.

These models can therefore be shared publicly without risk of revealing any sensitive information.


Related Work

General-purpose scripts for saving, loading, and using models are stored in a separate repository:

Script repository


Citation

If you use any model from this repository, please cite the corresponding study.
Please refer to the individual study folders for details.

Please also cite this repository: DOI

GitHub repository citation:

https://github.com/PetitPascal/Machine-learning-model


Contact

For questions regarding the models or associated studies:

Pascal Petit

email: pascal.petit@univ-grenoble-alpes.fr

ORCID: https://orcid.org/0000-0001-9015-5230

ResearchGate: https://www.researchgate.net/profile/Pascal-Petit-3

Google Scholar: https://scholar.google.fr/citations?user=ja8PT6MAAAAJ&hl=fr

Web Of Science: https://www.webofscience.com/wos/author/record/M-4351-2017

HAL: https://hal.science/search/index/q/*/authIdHal_s/pascal-petit

Thèse.fr: https://theses.fr/223750166

Current affiliation: Univ. Grenoble Alpes, CNRS, Grenoble INP*, LIG, 38000 Grenoble, France

*Institute of Engineering Univ. Grenoble Alpes

Former affiliations:

• Univ. Grenoble Alpes, AGEIS, 38000 Grenoble, France

• Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France

• CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000 Grenoble, France


Funding

My research has been partially supported by:

• The French government, through the National Research Agency (ANR - Agence Nationale de la Recherche), under the France 2030 program (MIAI Cluster), grant ANR-23-IACL-0006 (February 2025 – present).

• The French government, through the ANR, under the Investissements d’avenir program, grants ANR-10-AIRT-0005 and ANR-15-IDEX-0002 (September 2022 – April 2026).

MIAI@Grenoble Alpes, grant ANR-19-P3IA-0003 (October 2018 – January 2025).


If you find these models useful, please star this repository and cite the DOI in your research!

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Repository of trained machine learning models from multiple scientific studies, shared to support reproducibility, transparency, and external validation. Models are GDPR-compliant and do not include raw or personal data.

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