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When faced with self-regulation challenges, children have been known the usetheir language to inhibit their emotions and behaviors. Yet, to date, there hasbeen a critical lack of evidence regarding what patterns in their speechchildren use during these moments of frustration. In this paper, eXtremeGradient Boosting, Random Forest, Long Short-Term Memory Recurrent NeuralNetworks, and Elastic Net Regression, have all been used to forecast theselanguage patterns in children. Based on the results of a comparative analysisbetween these methods, the study reveals that when dealing withhigh-dimensional and dense data, with very irregular and abnormaldistributions, as is the case with self-regulation patterns in children,decision tree-based algorithms are able to outperform traditional regressionand neural network methods in their shortcomings.

Comparing Machine Learning-Centered Approaches for Forecasting Language Patterns During Frustration in Early Childhood