  Explainability is needed to establish confidence in machine learning results.
Some explainable methods take a post hoc approach to explain the weights of
machine learning models, others highlight areas of the input contributing to
decisions. These methods do not adequately explain decisions, in plain terms.
Explainable property-based systems have been shown to provide explanations in
plain terms, however, they have not performed as well as leading unexplainable
machine learning methods. This research focuses on the importance of metrics to
explainability and contributes two methods yielding performance gains. The
first method introduces a combination of explainable and unexplainable flows,
proposing a metric to characterize explainability of a decision. The second
method compares classic metrics for estimating the effectiveness of neural
networks in the system, posing a new metric as the leading performer. Results
from the new methods and examples from handwritten datasets are presented.
