  When training deep learning models for least-squares regression, we cannot
expect that the training error residuals of the final model, selected after a
fixed training time or based on performance on a hold-out data set, sum to
zero. This can introduce a systematic error that accumulates if we are
interested in the total aggregated performance over many data points. We
suggest to adjust the bias of the machine learning model after training as a
default postprocessing step, which efficiently solves the problem. The
severeness of the error accumulation and the effectiveness of the bias
correction is demonstrated in exemplary experiments.
