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Feature Ranking in Educational Data ML by Sergio Alumenda is a document available to read on EtoBox.
What is Feature Ranking in Educational Data ML about?
This document discusses using machine learning techniques like feature ranking algorithms and Shapley values to analyze an educational dataset and predict student performance. It evaluates different machine learning models on the full dataset and a reduced dataset selected by feature ranking. The ensemble models of bagged trees and AdaBoost achieved the best performance, with bagged trees reaching an accuracy of 81%.
- Author
- Sergio Alumenda
- Language
- EN