  Traditional regression models do not generalize well when learning from small
and noisy datasets. Here we propose a novel metamodel structure to improve the
regression result. The metamodel is composed of multiple classification base
models and a regression model built upon the base models. We test this
structure on the prediction of autism spectrum disorder (ASD) severity as
measured by the ADOS communication (ADOS COMM) score from resting-state fMRI
data, using a variety of base models. The metamodel outperforms traditional
regression models as measured by the Pearson correlation coefficient between
true and predicted scores and stability. In addition, we found that the
metamodel is more flexible and more generalizable.
