Combining Machine Learning Models using combo Library
This provides a practical tool for researchers and practitioners in machine learning to streamline ensemble methods, though it is incremental as it builds on existing libraries.
The authors tackled the challenge of model combination in ensemble learning by developing combo, a Python toolkit that aggregates models and scores for classification, clustering, and anomaly detection, resulting in a unified and accessible library with features like detailed documentation and easy installation.
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.