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

arXiv: https://arxiv.org/abs/2110.15778 · HTML: https://mlsanity.com/paper/2110.15778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Predicting Future Sales of Retail Products using Machine Learning](https://mlsanity.com/paper/2008.07779/) (2020)
- [Creutzfeldt-Jakob Disease Prediction Using Machine Learning Techniques](https://mlsanity.com/paper/2108.04972/) (2021)

## Abstract (arXiv)

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.