Science Score: 39.0%
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○CITATION.cff file
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✓DOI references
Found 2 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (13.5%) to scientific vocabulary
Keywords
Repository
Multi-Task Gradient Boosting
Basic Info
- Host: GitHub
- Owner: GAA-UAM
- License: lgpl-2.1
- Language: Python
- Default Branch: main
- Homepage: https://link.springer.com/chapter/10.1007/978-3-031-40725-3_9
- Size: 183 MB
Statistics
- Stars: 5
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
Multi-Task Gradient Boosting
The code and implementation for "Multi-Task Gradient Boosting" paper, as well as the datasets utilized, are housed in this repository.
The provided algorithm contains an implementation of the Multi-Task Gradient Boosting (MT-GB) algorithm, which extends the popular Gradient Boosting method for both classification and regression problems.
License
The package is licensed under the GNU Lesser General Public License v2.1.
Citation
When utilizing this package, kindly acknowledge it by citing as indicated below or employing the BibTeX format provided here.
yml
Emami, S., Ruiz Pastor, C., Martnez-Muoz, G. (2023). Multi-Task Gradient Boosting. In: Garca Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_9
Usage
The code for the proposed algorithm is available in this repository for multi-task regression and classification problems. The code is implemented in Python and uses the scikit-learn library.
To run the code, clone this repository and install the necessary libraries. Then, run the mtgb.py file to train and test the multi-task gradient boosting algorithm on the provided datasets.
Documentationd
To get started with this project, please refer to the Wiki."
Installation
To install the package, clone the repository and use pip to install.
bash
pip install .
Contributions
We warmly welcome contributions to the MT-GB! You can help enhance this algorithm by taking several actions, such as creating an issue to report a bug or suggest an improvement, forking the project and submitting a pull request to the development branch.
Key members of MT-GB
Gonzalo Martnez-Muoz, Carlos Ruiz Pastor, Seyedsaman Emami
Release Information
Version
0.0.1
Updated
08 May 2023
Date-released
08 May 2023
Owner
- Name: Grupo de Aprendizaje Automático - Universidad Autónoma de Madrid
- Login: GAA-UAM
- Kind: organization
- Location: Madrid, Spain
- Website: http://arantxa.ii.uam.es/~gaa/
- Repositories: 7
- Profile: https://github.com/GAA-UAM
Machine Learning Group at Universidad Autónoma de Madrid
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Dependencies
- numpy *
- scipy *
- sklearn *