Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning

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. Preprints 2020, 2020050354 (doi: 10.20944/preprints202005.0354.v1). Khoong, W.H. DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning. Preprints 2020, 2020050354 (doi: 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.

Supplementary and Associated Material

https://pypi.org/project/deboost/: PyPI repository for Python library/package
https://github.com/weihao94/DEBoost: GitHub repository for the scripts

Subject Areas

ensemble learning; machine learning; Python; spatial distance; statistical distance; weighted ensemble

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