关键词: 随机森林, 可视分析, 交互设计, 可解释机器学习

Abstract:

Random forests are typically applied in a black-box manner where the details of parameters tuning, training and even the final constructed model are hidden from the users in most cases. It leads to a poor model interpretability, which significantly hinders the model from being used in fields that require transparent and explainable predictions, such as medical diagnostics, justice, and security to some extent. The interpretation challenges stem from the randomicity of feature selection and data. Furthermore, random forests contain many decision trees, it is difficult or even impossible for users to understand and compare the structures and properties of all decision trees. To tackle these issues, an interactive visual analytics system FORESTVis is designed, it includes tree view, partial dependence plots, t-SNE projection, feature view and other interactive visual components. The researchers and practitioners of the model can intuitively understand the basic structures and working mechanism of random forests and assist users in evaluating the performance of models through interactive exploration. Finally, a case study using the Kaggle public dataset shows that the method is feasible and effective.

Key words: random forests, visual analysis, interaction design, interpretable machine learning