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Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At - Risk Students.
- Source :
-
Alinteri Journal of Agriculture Sciences . 2021, Vol. 36 Issue 1, p399-405. 7p. - Publication Year :
- 2021
-
Abstract
- Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 25647814
- Volume :
- 36
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Alinteri Journal of Agriculture Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 160131860
- Full Text :
- https://doi.org/10.47059/alinteri/V36I1/AJAS21060