aspg-21-1516-1791468724 20261008141204 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2023 20 2 Omar A. abd Alwahab Statistic Department, College of Administration and Economics, University of Diyala, Iraq In this study, the research aims to use some methods that deal with several independent variables with a dependent variable, where two methods were used to deal with, which is the method of slice inverse regression (SIR), which is considered a non-classical method, and two methods of machine learning, which is (TLBO, PSO), which is most popular of the teaching methods machine learning, the work of (SIR), (TLBO, PSO) is based on making reduced linear combinations of a partial set of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of multicollinearity between most of the explanatory variables. These new combinations of linear compounds resulting from the two methods will reduce the largest number of explanatory variables to reach one or more new dimensions called the effective dimension. The root mean square error criterion will be used to compare the two methods to indicate the preference of the methods. 2023 40 54 1516 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.200204 https://www.americaspg.com/journal/21/article/1516 https://www.americaspg.com/storage/71675818584.pdf