MODELING ACADEMIC PRODUCTIVITY USING ZERO-INFLATION AND MACHINE LEARNING METHODS FOR COUNT DATA
Keywords:
Zero-Inflation Models (ZIM), Machine Learning Algorithms, XGBoost, Random Forest, Neural NetworkAbstract
This research aims to investigate statistical models and machine learning techniques used to analyze numerical data suffering from overdispersion and a high proportion of excess zeros, common features in many fields such as insurance, healthcare, and the environment. In this study, we explore predictive modeling techniques for analyzing scientific productivity, measured by the number of articles published among biochemists. This study aims to evaluate and compare the predictive performance of classical zero-inflated regression models and modern machine learning algorithms in statistical data modeling, using academic productivity (number of published articles) as the main outcome variable. The dataset exhibits typical statistical data characteristics, including overdispersion and an excess of zeros. Therefore, several statistical models were considered, such as Poisson (POS), negative binomial (NB), zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB). As well, a collection of machine learning algorithms helps the use of the vector machines (SVMs), neural networks (NNs), random forests (RFs), and XGBoost were used to compare them. A number of evaluation metrics were used to assess the model performance:Root Mean Square Error (RMSE),Mean Absolute Error (MAE), R 2,Akaike Information Criterion (AIC)Bayesian Information Criterion (BIC).Log Likelihood (LogLik),Dispersion Index.The results showed that while traditional models such as ZINB and NB effectively handled overdispersion and excess zeros, Random Forest outperformed all models in terms of prediction accuracy, achieving the lowest RMSE and MAE values. SVM and ZIP followed with average performance, while XGBoost and NN showed relatively higher error rates without tuning.The results indicate that while statistical models remain valuable for their interpretability and clear handling of data characteristics, tree-based machine learning models, such as Random Forest, offer superior prediction performance for count data when prioritizing model accuracy.
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