Authors:
Ronny Hänsch
and
Olaf Hellwich
Affiliation:
Technische Universität Berlin, Germany
Keyword(s):
Random Forest, Randomized trees, Binary decision trees, Visualization.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Information and Scientific Visualization
;
Software Visualization
;
Visual Analytical Reasoning
Abstract:
Ensemble learning techniques and in particular Random Forests have been one of the most successful machine
learning approaches of the last decade. Despite their success, there exist barely suitable visualizations of
Random Forests, which allow a fast and accurate understanding of how well they perform a certain task and
what leads to this performance. This paper proposes an exemplar-driven visualization illustrating the most
important key concepts of a Random Forest classifier, namely strength and correlation of the individual trees
as well as strength of the whole forest. A visual inspection of the results enables not only an easy performance
evaluation but also provides further insights why this performance was achieved and how parameters of the
underlying Random Forest should be changed in order to further improve the performance. Although the paper
focuses on Random Forests for classification tasks, the developed framework is by no means limited to that
and can be easily applied
to other tree-based ensemble learning methods.
(More)