An Interactive Machine Learning Framework (1610.05463v1)

Abstract: Machine learning (ML) is believed to be an effective and efficient tool to build reliable prediction model or extract useful structure from an avalanche of data. However, ML is also criticized by its difficulty in interpretation and complicated parameter tuning. In contrast, visualization is able to well organize and visually encode the entangled information in data and guild audiences to simpler perceptual inferences and analytic thinking. But large scale and high dimensional data will usually lead to the failure of many visualization methods. In this paper, we close a loop between ML and visualization via interaction between ML algorithm and users, so machine intelligence and human intelligence can cooperate and improve each other in a mutually rewarding way. In particular, we propose "transparent boosting tree (TBT)", which visualizes both the model structure and prediction statistics of each step in the learning process of gradient boosting tree to user, and involves user's feedback operations to trees into the learning process. In TBT, ML is in charge of updating weights in learning model and filtering information shown to user from the big data, while visualization is in charge of providing a visual understanding of ML model to facilitate user exploration. It combines the advantages of both ML in big data statistics and human in decision making based on domain knowledge. We develop a user friendly interface for this novel learning method, and apply it to two datasets collected from real applications. Our study shows that making ML transparent by using interactive visualization can significantly improve the exploration of ML algorithms, give rise to novel insights of ML models, and integrates both machine and human intelligence.