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

TL;DR

This study compares machine learning methods to predict language patterns in children during frustration, finding decision tree-based algorithms outperform others in complex, irregular data scenarios.

Contribution

It provides a comparative analysis of ML approaches for forecasting children's language patterns during frustration, highlighting the effectiveness of decision tree-based methods.

Findings

Decision tree algorithms outperform neural networks and regression in complex data.

Decision tree methods handle high-dimensional, irregular data effectively.

The study advances understanding of machine learning applications in child language behavior.

Abstract

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.