GBMVis: Visual Analytics for Interpreting Gradient Boosting Machine

Abstract

The gradient boosting machine (GBM) composed of multiple weak learners is an efficient and widely used machine learning method. As a key factor in the prediction process of the gradient boosting machine, feature affects the performance of the gradient boosting machine when splitting nodes. The idea of GBM gives it a natural advantage to discover a variety of distinguishing features and feature combinations. Once the gradient boosting machine has the correct features, other factors play a relatively weak role. However, the GBM is a complicated and tedious process with diverse structure and attributes of decision tree, leading the model to be less interpretable, especially for high risk areas such as medical diagnosis and financial analytics that require transparent prediction. To tackle this issue, we have proposed an interactive visual analytic system, GBMVis, to help experts quickly analyze and of the gradient boosting machine. In addition to providing information about the features, we have also provided a visualization of the structure of boosting trees, which aims to display the major data flow in the gradient boosting machine. We have demonstrated the effectiveness of our system in a real dataset.