Random Forest Models for Data from an Informative Sample

Thomas M. Menino Convention & Exhibition Center 

Despite the fact that random forests are effective and flexible nonparametric models with many potential applications on survey data, until recently there were no methods for estimating them that accounted for data from an informative sample design. Since survey data is usually collected using an informative sample design, it is necessary to have an algorithm for creating random forest models that account for this design during model estimation. Because random forests are the result of averaging a large number tree models obtained from bootstrapped samples of the original data and obtaining bootstrap samples can be hard to produce for a general set o non-independent data, the sample design has been ignore.d. We investigate a recently proposed method to predict response burden to the US Bureau of Labor Statistics Consumer Expenditure Survey.

Sample Design

Surveys

Machine Learning

rpms

Response Burden

Consumer Expenditure Survey