Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparing Machine Learning-Centered Approaches for Forecasting Language
  Patterns During Frustration in Early Childhood   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.

21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg