Count Regression And Machine Learning Techniques For Zero-Inflated Overdispersed Count Data: Application To Ecological Data
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Date
2023Author
Sidumo, Bonelwa
Sonono, Energy
Takaidza, Isaac
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The aim of this study is to investigate the overdispersion problem that is rampant in ecological 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 performance 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 studies.