The relationship between parenting quality and children's social behaviour (e.g. gnawing) is also moderated by other variables, particularly the possible interaction between parental meta-emotional beliefs and children's temperament on children's socialisation development. This paper therefore uses machine learning to predict children's gnawing behaviour and uses the predictions to develop innovative designs for gnawing toys. This paper uses machine learning to identify and judge selection features. Machine learning includes random forest models and adaboost model (AM), and the AM is chosen to predict children's chewing behaviour through prediction performance and fitting result analysis. The paper begins with an analysis and description of the significant differences in mothers' behavioural responses and outcome measures, followed by an analysis of feature importance and prediction performance and fitting results, and finally an optimisation of the prediction model and toy design.

Lei Li. Prediction Children's Gnawing Behaviour Based on Machine Learning. Machine Learning Theory and Practice (2021), Vol. 2, Issue 3: 1-8. https://doi.org/10.38007/ML.2021.020301.

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