  Machine learning models for continuous outcomes often yield systematically
biased predictions, particularly for values that largely deviate from the mean.
Specifically, predictions for large-valued outcomes tend to be negatively
biased, while those for small-valued outcomes are positively biased. We refer
to this linear central tendency warped bias as the "systematic bias of machine
learning regression". In this paper, we first demonstrate that this issue
persists across various machine learning models, and then delve into its
theoretical underpinnings. We propose a general constrained optimization
approach designed to correct this bias and develop a computationally efficient
algorithm to implement our method. Our simulation results indicate that our
correction method effectively eliminates the bias from the predicted outcomes.
We apply the proposed approach to the prediction of brain age using
neuroimaging data. In comparison to competing machine learning models, our
method effectively addresses the longstanding issue of "systematic bias of
machine learning regression" in neuroimaging-based brain age calculation,
yielding unbiased predictions of brain age.
