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. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Možina, M., Demšar, J., Kattan, M., & Zupan, B. (2004). Nomograms for visualization of naive Bayesian classifier. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3202, 337–348. https://doi.org/10.1007/978-3-540-30116-5_32
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