Sonono, EnergySidumo, BonelwaTakaidza, Isaac2025-09-252023Sonono, Energy. et al. 2025. Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data. Annals of Data Science (2024) 11(3):803–817. [https://doi.org/10.1007/s40745-023-00464-6]https://doi.org/10.1007/s40745-023-00464-6http://hdl.handle.net/10394/43415Journal Article, Faculty of Natural and Agricultural Sciences, Potcheftroom Campus.The aim of this study is to investigate the overdispersion problem that is rampant in eco- logical count data. In order to explore this problem, we consider the most commonly used count regression models: the Poisson, the negative binomial, the zero-inflated Poisson and the zero-inflated negative binomial models. The performance of these count regression models is compared with the four proposed machine learning (ML) regression techniques: random forests, support vector machines, k−nearest neighbors and artificial neural networks. The mean absolute error was used to compare the per- formance of count regression models and ML regression models. The results suggest that ML regression models perform better compared to count regression models. The performance shown by ML regression techniques is a motivation for further research in improving methods and applications in ecological studiesenCount data · Ecology · Machine learning · Overdispersion · Zero-inflationArticle