A study on interpretability of decision of machine learning
Shohei Shirataki, Saneyasu Yamaguchi · 2017
Machine learning is one of the most important fields in recent improvement in big data analysis. Many people apply machine learning for a variety of domains for various purposes, such as classification of opinions. However, the constructed models of machine learning are black boxes. They cannot understand the background reason for their decisions. In many cases, understanding the reasons important. In this paper, we focus on interpretation of models and understanding of decision reasons. First, we introduce the results of an opinions classification of the reviews with Support Vector Machine (SVM). Second, we interpret the model by analyzing weights of the model. Third, we introduce a method for helping to understand the reasons for a decision by SVM by providing a simplified information of the highly weighted words.