  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.
