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A general procedure for evaluating models and ensemble Support Vector Regression
Guoyi Zhang1,2, Yulei He1
1National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland, USA.
Abstract:
In practice, we may want to discover if there is a relationship between a response variable as a function of the predictor variables. Multiple linear regression (MLR) is a popular tool for such purpose. When the relationship is nonlinear, nonparametric regression methods such as local linear regression, smoothing splines, and support vector regression (SVR) provide flexible alternatives to MLR. How do we compare the performance of these methods and choose an appropriate one for use? In this research, we propose a general procedure to evaluate the performance of different regression methods for use in large data. We also propose an ensemble SVR for regression analysis. The proposed methods are applied to address research questions using the Research and Development Survey, conducted by the National Center for Health Statistics.
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