# Combining Machine Learning Models using combo Library

arXiv: https://arxiv.org/abs/1910.07988 · HTML: https://mlsanity.com/paper/1910.07988/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library](https://mlsanity.com/paper/2406.05999/) (2024)

## Abstract (arXiv)

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.