Visualizing predictive models in decision tree generation

This paper discusses a visualization technique integrated with inductive generalization. The technique represents classification rules inferred from data, as landscapes of graphical objects in a 3D visualization space, which can provide valuable insights into knowledge discovery and model-building processes. Such visual organization of classification rules can contribute to additional human insights into classification models that are hard to attain using traditional displays. It also includes navigational locomotion and high interactivity to facilitate the interpretation and comparison of results obtained in various classification scenarios. This is especially apparent for large rule sets where browsing through textual syntax of thousands of rules is beyond human comprehension. Visualization of both knowledge and data aids in assessing data quality and provides the capability for data cleansing.