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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Franklin Parrales-Bravo, Roberto Tolozano-Benites, Alexander Castro-Mora, Leonel Vasquez-Cevallos, Elsy Rodríguez-Revelo
Format: Article
Language:English
Published: University of New Mexico 2025-05-01
Series:Neutrosophic Sets and Systems
Subjects:
random forest
neutrosophic logic
academic performance prediction
educational data mining
prediction confidence
supervised learning
Online Access:https://fs.unm.edu/NSS/40.%20RandomForestWord.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://fs.unm.edu/NSS/40.%20RandomForestWord.pdf

Similar Items