SUPERVISED MACHINE LEARNING: A COMPARISON OF POISSON AND NEGATIVE BINOMIAL REGRESSION FOR COUNT DATA ANALYSIS Article Swipe
This study explores the application of supervised machine learning techniques, specifically Poisson and negative binomial regression models, for analyzing count data to forecast outgoing mail volume for the General Directorate of Posts of Saudi Arabia from 2002 to 2006. The dataset covers 13 administrative regions and consists of 65 observations with 3 variables - the dependent variable is the number of outgoing mails, and the independent variables are year and region. Exploratory data analysis revealed significant overdispersion in the data, with a large number of zero observations. Initial Poisson regression analysis highlighted the model’s limitations in addressing these data characteristics. In contrast, the negative binomial regression model demonstrated superior performance, achieving a lower Mean Absolute Prediction Error (MAPE) of 34,026.7 compared to 34,253.08 for the Poisson model. Additionally, likelihood-based metrics such as the Likelihood Ratio Test, AIC, and BIC consistently indicated that the negative binomial regression model provided a better fit to the data, reflecting the underlying overdispersion. Based on these findings, the negative binomial regression model is recommended as the primary approach for predicting outgoing mail volume for the General Directorate of Posts of Saudi Arabia.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17654/0972361725040
- https://pphmjopenaccess.com/index.php/aas/article/download/2355/1581
- OA Status
- hybrid
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410452994
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- OpenAlex ID
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https://openalex.org/W4410452994Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17654/0972361725040Digital Object Identifier
- Title
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SUPERVISED MACHINE LEARNING: A COMPARISON OF POISSON AND NEGATIVE BINOMIAL REGRESSION FOR COUNT DATA ANALYSISWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-16Full publication date if available
- Authors
-
Walaa Ahmed HamdiList of authors in order
- Landing page
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https://doi.org/10.17654/0972361725040Publisher landing page
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https://pphmjopenaccess.com/index.php/aas/article/download/2355/1581Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://pphmjopenaccess.com/index.php/aas/article/download/2355/1581Direct OA link when available
- Concepts
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Count data, Poisson regression, Negative binomial distribution, Statistics, Poisson distribution, Binomial (polynomial), Regression analysis, Mathematics, Computer science, Machine learning, Artificial intelligence, Medicine, Environmental health, PopulationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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