Pattern Aided Explainable Machine Learning

Abstract

Interpretability has been recognized as an important property of machine learning models. Lack of interpretability brings challenges for the deployments of many black models such as random forest, support vector machine (SVM) and neural networks. One aspect of interpretability is the ability to provide explanations for the predictions of a model, and explanations help users to understand the logical reasoning behind a model, thus giving users greater confidence to accept/reject predictions. Explanations are useful and sometimes even mandatory in domains like medical analysis, marketing, and criminal investigations, where decisions based on the predictions may have severe consequences. Trad..