Combining Machine Learning Models Using combo Library


  • Yue Zhao Carnegie Mellon University
  • Xuejian Wang Carnegie Mellon University
  • Cheng Cheng Carnegie Mellon University
  • Xueying Ding Carnegie Mellon University



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 {}.




How to Cite

Zhao, Y., Wang, X., Cheng, C., & Ding, X. (2020). Combining Machine Learning Models Using combo Library. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13648-13649.