  Traditional machine learning (ML) algorithms, such as multiple regression,
require human analysts to make decisions on how to treat the data. These
decisions can make the model building process subjective and difficult to
replicate for those who did not build the model. Deep learning approaches
benefit by allowing the model to learn what features are important once the
human analyst builds the architecture. Thus, a method for automating certain
human decisions for traditional ML modeling would help to improve the
reproducibility and remove subjective aspects of the model building process. To
that end, we propose to use shape metrics to describe 2D data to help make
analyses more explainable and interpretable. The proposed approach provides a
foundation to help automate various aspects of model building in an
interpretable and explainable fashion. This is particularly important in
applications in the medical community where the `right to explainability' is
crucial. We provide various simulated data sets ranging from probability
distributions, functions, and model quality control checks (such as QQ-Plots
and residual analyses from ordinary least squares) to showcase the breadth of
this approach.
