Nomograms for visualization of naive Bayesian classifier

Authors:
Martin Možina;Janez Demšar;Michael Kattan;Blaž Zupan
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Memorial Sloan Kettering Cancer Center, New York, NY;Faculty of Computer and Information Science, University of Ljubljana, Slovenia and Baylor College of Medicine, Houston, TX
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

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Abstract

Besides good predictive performance, the naive Bayesian classifier can also offer a valuable insight into the structure of the training data and effects of the attributes on the class probabilities. This structure may be effectively revealed through visualization of the classifier. We propose a new way to visualize the naive Bayesian model in the form of a nomogram. The advantages of the proposed method are simplicity of presentation, clear display of the effects of individual attribute values, and visualization of confidence intervals. Nomograms are intuitive and when used for decision support can provide a visual explanation of predicted probabilities. And finally, with a nomogram, a naive Bayesian model can be printed out and used for probability prediction without the use of computer or calculator.