Subgroup discovery (SD) is a supervised machine learning method used for exploratory data analysis to identify relationships (subgroups) within a dataset in relation to a target variable. Key components of SD algorithms include the search strategy, which explores the problem search space, and the quality measure, which evaluates the identified subgroups. Despite the power of SD and the variety of algorithms available, only a few Python libraries offer state-of-the-art SD tools. Existing libraries such as Vikamine and by subgroups lack comprehensive support, highlighting the need for a reliable and well-documented library that integrates popular SD algorithms.
Researchers from the Med ai Lab at the University of Murcia and the Murcian Institute of Biosalud have presented Subgroups, an open-source Python library designed to simplify SD algorithms. Built to be efficient in native Python, the library provides an easy-to-use interface modeled after scikit-learn, making it accessible to experts and non-experts alike. The library ensures reliable algorithm implementations based on established scientific research, and its modular design allows for customization and expansion. Subgroups is already employed in multiple papers and research projects and is available on GitHub, PyPI, and Anaconda.org.
The Subgroup Library is a modular Python tool designed for SD algorithms, following an architecture with core elements, quality measures, data structures, and algorithms. It includes classes for key SD components such as selectors, patterns, and subgroups. The library implements several SD algorithms such as VLSD and SDMap along with multiple quality measures including WRAcc and Binomial Tests. It supports silent and logging modes for flexible output and offers extensive unit testing to ensure correct functionality. Built with Python 3 and leveraging pandas, the library is designed for easy extension and reliable algorithm performance.
The subset library offers a comprehensive ecosystem of manuals and examples that allow users and developers to become familiar with SD techniques and the library implementation. It provides practical examples, such as the VLSD algorithm, and is open source, allowing researchers to apply key SD algorithms across multiple domains. This versatility allows the library to be used in both past and current research where SD tools were previously unavailable, and contributes to generating new scientific insights.
In addition to being a valuable resource for research, the library is also used in real-world projects, having been downloaded over 7100 times and featured in several scientific papers. It allows for a fair comparison and evaluation of machine learning algorithms within a unified framework, avoiding the need to combine multiple machine learning libraries. The subset library is continuously evolving and offers the potential for further expansion and integration of new algorithms. It has already been applied in several notable research projects and collaborations, demonstrating its growing impact in academic and practical contexts.
The Subgroups Library is an open-source Python tool that simplifies the use of SD algorithms in machine learning and data science. Its key features include increased efficiency due to its native Python implementation, a user-friendly interface based on scikit-learn, and trusted algorithm implementations based on scientific publications. The library’s modular design allows for easy customization, allowing users to add new algorithms, quality measures, and data structures. It has already been applied in numerous papers and research projects, highlighting its effectiveness and adaptability across multiple domains. Future updates will include additional SD algorithms and search strategies.
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