Combining Machine Learning Models using combo Library

TL;DR

This paper introduces 'combo', a Python toolkit that simplifies the process of combining various machine learning models across different tasks, supporting models from multiple libraries with a unified interface.

Contribution

The paper presents an easy-to-use, unified Python library for model combination that supports multiple ML tasks and integrates models from popular libraries, enhancing accessibility and robustness.

Findings

Supports classification, clustering, and anomaly detection scenarios.

Compatible with models from scikit-learn, XGBoost, LightGBM.

Designed for accessibility, robustness, and ease of use.

Abstract

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.