Predicting ESRB ratings for video games by content using decision trees

• Autores: Huamán Maqque, Rodrigo Sebastián, Condori Anahua, Graciela, Rojas Carhuas, Fátima Gigi, Quispe Huacho, Rodolfo Robert
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/83
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/83; https://doi.org/10.48168/innosoft.s11.a83; https://purl.org/42411/s11/a83; https://n2t.net/ark:/42411/s11/a83
• Nivel de acceso: acceso abierto
• Materia: Decision tree; Data mining; ESRB; Árbol de decisión; Minería de datos

Various studies have proven that the levels of violence in video games can negatively influence the development of children, especially in adolescence and that is why care must be taken that the classification is appropriate according to the content present. For the analysis, the ESRB classification was used, which contains 7 different categories, together with the implementation of a decision tree model, which is a data mining technique capable of graphically representing the relationship between the variables. The results showed that the precision level for a level 6 tree does not exceed the minimum required.