PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods

Year 2024, Volume: 7 Issue: 5, 971 - 981, 15.09.2024

Tohid Yousefi , Özlem Varlıklar

https://doi.org/10.34248/bsengineering.1467132

Abstract

Feature selection is a significant data mining and machine learning technique that enhances model performance by identifying important features within a dataset, reducing the risk of overfitting while aiding the model in making faster and more accurate predictions. Pyallffs is a Python library developed to optimize the feature selection process, offering rich content and low dependency requirements. With 19 different filtering methods, pyallffs assists in analyzing dataset features to determine the most relevant ones. Users can apply custom filtering methods to their datasets using pyallffs, thereby achieving faster and more effective results in data analytics and machine learning projects. The source codes, supplementary materials, and guidance is publicly available on GitHub: https://github.com/tohid-yousefi/pyallffs.

Keywords

Feature selection, Filter methods, Feature selection library, Open-source library, Python software

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