Oneofthe most important issues in various sciences is classification. Logistic regression is one of the statistical methods for data classification in which the distribution of data is supposed to be known. In addition to statistical methods, researchers are now using other methods to classify data such as machine learning algorithms that do not require the data distribution to be known. In this paper, logistic regression and some machine learning algorithms including CART, random forest, Bagging and Boosting of supervising learning are discussed. Using four real data sets and a simulation example, we compare the performance of the logistic regression with machine learning algorithms in terms of accuracy, sensitivity and precision measures.
Karami, T., Izad i, M. & Niaparast, M. (2021). Comparison of logistic regression with some machine learning
methods in classifying data. Andishe_ye Amari, 26(1), 47-59.
MLA
Karami, T., Izad i, M., & Niaparast, M. "Comparison of logistic regression with some machine learning
methods in classifying data", Andishe_ye Amari, 26, 1, 2021, 47-59.
HARVARD
Karami T., Izad i M., Niaparast M. (2021). 'Comparison of logistic regression with some machine learning
methods in classifying data', Andishe_ye Amari, 26(1), pp. 47-59.
CHICAGO
T. Karami, M. Izad i & M. Niaparast, "Comparison of logistic regression with some machine learning
methods in classifying data," Andishe_ye Amari, 26 1 (2021): 47-59,
VANCOUVER
Karami T., Izad i M., Niaparast M. Comparison of logistic regression with some machine learning
methods in classifying data. Andishe_ye Amari. 2021;26(1):47-59 (In Persian).
