  The Majorana Demonstrator is a leading experiment searching for neutrinoless
double-beta decay with high purity germanium detectors (HPGe). Machine learning
provides a new way to maximize the amount of information provided by these
detectors, but the data-driven nature makes it less interpretable compared to
traditional analysis. An interpretability study reveals the machine's
decision-making logic, allowing us to learn from the machine to feedback to the
traditional analysis. In this work, we have presented the first machine
learning analysis of the data from the Majorana Demonstrator; this is also the
first interpretable machine learning analysis of any germanium detector
experiment. Two gradient boosted decision tree models are trained to learn from
the data, and a game-theory-based model interpretability study is conducted to
understand the origin of the classification power. By learning from data, this
analysis recognizes the correlations among reconstruction parameters to further
enhance the background rejection performance. By learning from the machine,
this analysis reveals the importance of new background categories to
reciprocally benefit the standard Majorana analysis. This model is highly
compatible with next-generation germanium detector experiments like LEGEND
since it can be simultaneously trained on a large number of detectors.
