Classification the Number of Children Ever Born using CART Model

Background and Objective: Discriminant analysis and logistic regression are classical methods for classifying data in several studies. However, these models do not lead in valid results due to not meeting all necessary assumptions. The purpose of this study was to classify the number of Children Ever Born (CEB) using decision tree model in order to present an efficient method to classify demographic data. Methods: In the present study, CART tree model with Gini splitting rule was fitted to classify the number of CEB in fertility behavior of at least once married ۱۵-۴۹ year-old women, in Semnan-۲۰۱۲. ۴۰۵ women aged ۱۵-۴۹ years old comprised the survey sample. Results: Women in first and second birth cohorts who had married at an early age had ۳ CEB while women who had married at an older age had ۲ CEB. Women in third birth cohort who had married at an early age and were employed, had ۲ CEB while unemployed women in this cohort whose type of marriages were familial and non-familial had ۰ and ۱ CEB respectively. Women in the third birth cohort who were married in older age had ۱ CEB. Conclusion: Among important advantages of CART model are the simplicity in interpretation, using distribution-free measures, considering missing data and outliers for construction trees which has increased the usage of this method. Therefore, this method is a suitable way for classifying demographic data in comparison to other classical modeling methods in the conditions that necessary assumptions are not met.

Keywords:

Classification , Decision Tree , CART Model , Gini Splitting Rule , Children Ever Born (CEB) , طبقه بندی , درخت تصمیم , مدل کارت , شاخص افراز جینی , تعداد فرزندان زنده به دنیا آمده.