Version 1
: Received: 22 May 2020 / Approved: 23 May 2020 / Online: 23 May 2020 (04:54:39 CEST)
How to cite:
Khoong, W. H. DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning. Preprints2020, 2020050354. https://doi.org/10.20944/preprints202005.0354.v1
Khoong, W. H. DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning. Preprints 2020, 2020050354. https://doi.org/10.20944/preprints202005.0354.v1
Khoong, W. H. DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning. Preprints2020, 2020050354. https://doi.org/10.20944/preprints202005.0354.v1
APA Style
Khoong, W. H. (2020). DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning. Preprints. https://doi.org/10.20944/preprints202005.0354.v1
Chicago/Turabian Style
Khoong, W. H. 2020 "DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning" Preprints. https://doi.org/10.20944/preprints202005.0354.v1
Abstract
In this paper, we introduce deboost, a Python library devoted to weighted distance ensembling of predictions for regression and classification tasks. Its backbone resides on the scikit-learn library for default models and data preprocessing functions. It offers flexible choices of models for the ensemble as long as they contain the predict method, like the models available from scikit-learn. deboost is released under the MIT open-source license and can be downloaded from the Python Package Index (PyPI) at https://pypi.org/project/deboost. The source scripts are also available on a GitHub repository at https://github.com/weihao94/DEBoost.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.