  When faced with self-regulation challenges, children have been known the use
their language to inhibit their emotions and behaviors. Yet, to date, there has
been a critical lack of evidence regarding what patterns in their speech
children use during these moments of frustration. In this paper, eXtreme
Gradient Boosting, Random Forest, Long Short-Term Memory Recurrent Neural
Networks, and Elastic Net Regression, have all been used to forecast these
language patterns in children. Based on the results of a comparative analysis
between these methods, the study reveals that when dealing with
high-dimensional and dense data, with very irregular and abnormal
distributions, as is the case with self-regulation patterns in children,
decision tree-based algorithms are able to outperform traditional regression
and neural network methods in their shortcomings.
