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Logistic regression in data analysis: an overview

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  • Maher Maalouf

Abstract

Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and machine learning perspective and how LR can be applied to imbalanced and rare events data.

Suggested Citation

  • Maher Maalouf, 2011. "Logistic regression in data analysis: an overview," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 281-299.
    • Handle: RePEc:ids:injdan:v:3:y:2011:i:3:p:281-299
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    Cited by:

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    2. Carmen Patino-Alonso & Marta Gómez-Sánchez & Leticia Gómez-Sánchez & Benigna Sánchez Salgado & Emiliano Rodríguez-Sánchez & Luis García-Ortiz & Manuel A. Gómez-Marcos, 2022. "Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
    3. Sumita, Kazuto & Nakazawa, Katsuyoshi & Kawase, Akihiro, 2021. "Long-term care facilities and migration of elderly households in an aged society: Empirical analysis based on micro data," Journal of Housing Economics, Elsevier, vol. 53(C).
    4. Amgad Muneer & Suliman Mohamed Fati, 2020. "A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter," Future Internet, MDPI, vol. 12(11), pages 1-20, October.
    5. Sheunesu Brandon Shamuyarira & Trust Tawanda & Elias Munapo, 2023. "Truck Fuel Consumption Prediction Using Logistic Regression and Artificial Neural Networks," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 14(1), pages 1-17, January.

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