Evaluating Performance Of Identifying At - Risk Students And Learning Achievement Model using Accuracy And F-measure by Comparing Logistic Regression, Generalized Linear Model And Gradient Boost Machine Algorithm - Harini, K.; Rekha, K K. Sashi (2022 International Conference for Advancement in Technology (ICONAT), 2022)
Aim: The objective of the study is to improve the Accuracy and F-Measure percentage of At - risk students Model on the basis of High withdrawal and Failure rate and learning Achievement Model based on low performance and less motivational status. Materials and Methods: Logistic Regression with sample size = 20, Generalised Linear Model (GLM) with sample size = 20 and Gradient Boost Machine algorithm (GBM) with sample size = 20 was executed a different number of times for predicting accuracy and F-Measure percentage of At - risk students Model and Learning Achievement Model. For improving the Accuracy and F-measure percentage the Novel sigmoid function was used in Logistic Regression which helps in mapping the prediction to probabilities. Results and Discussion: Logistic Regression achieved significantly better Accuracy (95.61%), F-Measure (93.65%) compared to GLM Accuracy (92.97%), F-Measure (89.45%) in At-Risk students Model and in Learning Achievement Model Logistic Regression has better accuracy (96.37%), F-Measure (95.59%) when compared to GBM accuracy (91.73%), F-Measure (90.85%). There was a statistical significance between Logistic regression, GLM and GBM (p = 0.000) (p < 0.05). Conclusion: In this study it is found that the Logistic Regression algorithm performed better than the GLM algorithm and GBM Algorithm for predicting accuracy, F-Measure percentage of At - risk students Model and Learning Achievement Model using Novel Sigmoid function.