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.

机器学习结果的可解释性需要建立信心；对权重进行解释的方法不足以用简单的术语解释决策；可解释的属性系统提供了术语解释，但表现不如不可解释的机器学习方法；本研究侧重于解释性指标的重要性，提出了两种能够提高性能的方法：第一种方法引入可解释性与不可解释性流程相结合，并提出了一个刻画决策可解释性的指标；第二种方法比较了用于估计系统中神经网络效果的经典指标，并提出了一种新的效果最佳的指标；本文展示了新方法的结果和手写数据集的示例。