Performance Comparison of Logistic Regression and Classification Regression tree Models for Binary Dependent Variable

This paper describes the performance analysis of two classifier models common in statistics and data mining on binary dependent variable, binary Logistic Regression (B.LR) and Classification Regression Tree (CART). The evaluation method is using all data in training stage. The using data set is from “Evaluation of patients with Jaundice on children” report. Data set is collection of categorical and continues independent variables. The classification performance of two classifiers is presented by using statistical performance measures like accuracy, specificity and sensitivity. Experimental result showed that accuracy of LR is more than 83% and CLASSIFICATION AND REGRESSION TREE is nearly 73%. So the sensitivity measure for BINARY LOGISTIC REGRESSION is nearby 77% and 66% for CLASSIFICATION AND REGRESSION TREE as well the specificity scale is 85% for BINARY LOGISTIC REGRESSION and 76% for CLASSIFICATION AND REGRESSION TREE. The result shows the performance of BINARY LOGISTIC REGRESSION classifier is found to be better than CLASSIFICATION AND REGRESSION TREE.