Binary Classification with Supervised Machine Learning: A Comparative Analysis

Authors

Yassine El aachab, Laboratory of Analysis Geometry and Applications, Department of Mathematics, Faculty of Sciences, Ibn Tofail University Kenitra, MoroccoFollow
Mohammed Kaicer, Laboratory of Analysis Geometry and Applications, Department of Mathematics, Faculty of Sciences, Ibn Tofail University Kenitra, MoroccoFollow
Youness Jouilil, Department of Economics, Faculty of Economics and Social Sciences of Mohammedia, Hassan II University of Casablanca, MoroccoFollow

Author Country (or Countries)

Morocco

Abstract

Over the past decade, Machine Learning has become a practical approach for simulating and examining social issues, notably poverty, education, and health diseases. This study compares the performance of various machine learning methods especially Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) in predicting poverty status. For this purpose, the present contribution employs a micro dataset which has been extracted from the National Survey on Household Consumption and Expenditure 2013/2014. Several evaluation metrics such as accuracy, precision, Cohen’s Kappa statistic, F1-score, and recall are used to evaluate the models’ outputs. The R results indicate that the three algorithms achieved high accuracy scores. Therefore, the decision trees have more improvements in terms of accuracy (99.61%) compared to LR (91.09%) and linear kernel SVM methods ( 99.24%).

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/170407

Recommended Citation

El aachab, Yassine; Kaicer, Mohammed; and Jouilil, Youness (2023) "Binary Classification with Supervised Machine Learning: A Comparative Analysis," Applied Mathematics & Information Sciences: Vol. 17: Iss. 4, Article 7.
DOI: http://dx.doi.org/10.18576/amis/170407
Available at: https://digitalcommons.aaru.edu.jo/amis/vol17/iss4/7