  We describe the use of machine learning algorithms to select high-quality
measurements for the Mu2e experiment. This technique is important for
experiments with backgrounds that arise due to measurement errors. The
algorithms use multiple pieces of ancillary information that are sensitive to
measurement quality to separate high-quality and low-quality measurements.
