Combining Machine Learning Models using combo Library
Abstract
Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.
Cite
@article{arxiv.1910.07988,
title = {Combining Machine Learning Models using combo Library},
author = {Yue Zhao and Xuejian Wang and Cheng Cheng and Xueying Ding},
journal= {arXiv preprint arXiv:1910.07988},
year = {2020}
}
Comments
In Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)