Detecting the risk of bullying victimization among adolescents: A large-scale machine learning approach

There is an increasing interest in using machine learning methods to identify risk factors for problematic behaviors. The current study tested and compared six machine learning algorithms: Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Light Gradient B...

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Veröffentlicht in:	Computers in human behavior Jg. 147; S. 107817
Hauptverfasser:	Yan, Wei, Yuan, Yidan, Yang, Menghao, Zhang, Peng, Peng, Kaiping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.10.2023
Schlagworte:	Adolescents Cyberbullying victimization Machine learning Risk factors Traditional bullying victimization Traditional bullying victimization Adolescents Risk factors Cyberbullying victimization Machine learning
ISSN:	0747-5632, 1873-7692
Online-Zugang:	Volltext
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