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Visualizing class probability estimators

Abstract
Inducing classifiers that make accurate predictions on future data is a driving force for research in inductive learning. However, also of importance to the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate "black boxes"—that is, the representation of the model makes it virtually impossible to gain any insight into what has been learned. This paper presents a technique that can help the user understand why a classifier makes the predictions that it does by providing a two-dimensional visualization of its class probability estimates. It requires the classifier to generate class probabilities but most practical algorithms are able to do so (or can be modified to this end).
Type
Working Paper
Type of thesis
Series
Computer Science Working Papers
Citation
Frank, E. & Hall, M. (2003). Visualizing class probability estimators. (Working paper 02/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Date
2003-02-19
Publisher
University of Waikato, Department of Computer Science
Degree
Supervisors
Rights