Using XG Boost and Random Forest Classifier Algorithms to Predict Student Behavior

Nguyen, Ha Thi The; Chen, Ling-Hsiu; Saravanarajan, Vani Suthamathi; Pham, Huy Quang

doi:10.1109/eti4.051663.2021.9619217

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The formula for calculating the recall value is in equation (7). 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃+𝐹𝑁 (7) Accuracy is used to see the ratio that is correctly predicted with all data using the formula in equation ( 8) [39].…”

Section: Discussionmentioning

confidence: 99%

Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree

Nadya,

Ferdiansyah,

Nastiti

et al. 2024

SJI

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Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models.Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%.Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.

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Section: Discussionmentioning

confidence: 99%