Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence

This study proposes a hybrid approach to predict students’ final academic performance in a mathematics course by integrating Random Forest, a supervised machine learning model, with neutrosophic logic to assess prediction reliability. The objective is to improve educational forecasting by not only p...

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Bibliografiske detaljer

Principais autores:	Franklin Parrales-Bravo, Roberto Tolozano-Benites, Alexander Castro-Mora, Leonel Vasquez-Cevallos, Elsy Rodríguez-Revelo
Format:	Artigo
Sprog:	Inglês
Udgivet:	University of New Mexico 2025-05-01
Serier:	Neutrosophic Sets and Systems
Fag:	random forest neutrosophic logic academic performance prediction educational data mining prediction confidence supervised learning
Online adgang:	https://fs.unm.edu/NSS/40.%20RandomForestWord.pdf
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