Abstract
Mainstream machine learning approaches to predictive analytics consistently prove their ability to perform well using a variety of datasets, although the task of identifying an optimally-performing machine learning approach for any given dataset becomes much less intuitive. Methods such as ensemble and transformation modeling have been developed to improve upon individual base learners and datasets with large degrees of variance. Despite the increased generalizability and flexibility of ensemble approaches, the cost often involves sacrificing inference for predictive ability. This paper introduces an alternative approach to ensemble modeling, combining the predictive ability of an ensemble framework with localized model construction through the incorporation of cluster analysis as a pre-processing technique. The workflow not only outperforms independent base learners and comparative ensemble methods, but also preserves local inferential capability by manipulating cluster parameters and maintaining interpretable relative importance values and non-transformed coefficients for the overall consideration of variable importance. This paper demonstrates the ensemble technique on a dataset to estimate rates of health insurance coverage across the state of Missouri, where the cluster pre-processing assists in understanding both local and global variable importance and interactions when predicting high concentration areas of low health insurance coverage based on demographic, socioeconomic, and geospatial variables.
Highlights
The ability to simultaneously model and analyze both local and global relationships in geospatially-referenced statistical models is a challenge commonly faced by social science researchers, geographers and spatial statisticians
We found that models with fewer than four clusters had a progressive increase in mean square error (MSE), which was likely due to the increased heterogeneity in the larger clusters
Following the support vector regression category, the best-performing category consisted of dimension reduction methods, with principal components regression (PCR) and partial least squares (PLS) producing the two smallest MSE values, respectively (0.487, 0.488)
Summary
The ability to simultaneously model and analyze both local and global relationships in geospatially-referenced statistical models is a challenge commonly faced by social science researchers, geographers and spatial statisticians. Methods common to social scientists, such as linear and logistic regression, are limited in that they only model global relationships—entire datasets—and produce results that can only be attributed to the dataset as a whole. Other methods, such as clustering techniques, are often used to identify concentrations or “hotspots” within or between variables in a dataset, but fail to provide information about intervariable relationships or dependencies, which can be accomplished through regression analysis [1]. Building on the work of Trivedi et al [2,3,4] this study makes a unique contribution to the literature by using the clustering method as a pre-processing technique to geospatial data and regression techniques to examine global dataset trends, while inferring local intervariable relationships
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
