A Visual Analytics Technique to Compare the Performance of Predictive Models

Decisions that people make every day are affected by the information available in a given moment. Predictive models are used to estimate future values. For a given set of data and an analysis goal, the results of the models can vary, so it is important to select the most accurate model for the set of data. This paper proposes a Visual Analytics technique for comparing the performance of predictive models. It consists of four main components that support the tasks of the Keim’s Visual Analytics Mantra: “analyze first, show the important, zoom, filter and analyze further, details on demand”. The first component, analyze data, by building predictive models using various machine learning algorithms; the other three components are interactive visualizations that show the important results found by the models, zoom and filter on results of interest and finally, further analyze the selected results by showing details on the data.