Combining Machine Learning Models Using combo Library

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 uni- fied 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.

Authors

• Yue Zhao
• Xuejian Wang
• Cheng Cheng State Key Laboratory of Cognitive Intelligence, Hefei, China
• Xueying Ding

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