Data Imputation by Random Forest
- The Principle and Its Application for National Center Test in Japan -

Abstract

Random Forest, one of the ensemble learning methods for classification and non-linear regression model, provides a stable and an accurate data imputation for the missing data. This paper shows that the algorithm works well for a large dataset containing missing data. The examples are science and society examination scores appearing in the Japanese National Center Test in 200x.