00000cab a22000004cb4500 IPP-00000265301 IPP 20190704150636.0 190704s2016 xx d | ||r |||||eng|| eng Lucagbo, Michael Daniel C. Comparison of ordinal logistic regression with tree-based methods in predicting socioeconomic classes in the Philippines. IN The Philippine Statistician [column] Comparison of ordinal logistic regression with tree-based methods in predicting socioeconomic classes in the Philippines. IN The Philippine Statistician [column] 2016 tables, formulas The task of classifying Philippine households according to their socioeconomic class (SEC) has been tackled anew in a collaborative work between the Marketing and Opinion Research Society of the Philippines (MORES), the former National Statistics Office (NSO) and the University of the Philippines School of Statistics. This new system of classifying Philippine households has been introduced in the 12th National Convention on Statistics, in a paper entitled 1SEC 2012: The New Philippine Socioeconomic Classification. To predict the SEC of a household, certain household characteristics are used as predictors. The 1SEC Instrument, whose scoring system is based on the ordinal logistic regression model, is then used to predict the household's SEC. Recently, the statistical literature has seen the development of novel tree-based learning algorithms. This paper shows that the ordinal logistic regression model can still classify households better than three popular tree-based statistical learning method: bootstrap aggregation (or nagging), random forests, and boosting. In addition, this paper identifies which clusters are easier to predict than others. Regression analysis Statistics on households The Philippine Statistician Vol. 65, no. 1 (2016), 1-14 UPD DIS Article FI