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Permanent link (DOI): https://doi.org/10.7939/R36D5PH6N

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Explaining Naive Bayes Classifications Open Access

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Author or creator
Szafron, Duane
Greiner, Russ
Lu, Paul
Wishart, David
Macdonell, Cam
Anvik, J.
Poulin, B.
Lu, Z.
Eisner, Roman
Additional contributors
Subject/Keyword
Proteome Analyst
Naive Bayes
Machine Learning
Bioinformatics
Explain
Type of item
Computing Science Technical Report
Computing science technical report ID
TR03-09
Language
English
Place
Time
Description
Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a novel graphical explanation facility for Naive Bayes classifiers that serves three purposes. First, it transparently explains the reasoning used by the classifier to foster user confidence in the prediction. Second, it enhances the user's understanding of the complex relationships between the features and the labels. Third, it can help the user to identify suspicious training data. We demonstrate these ideas in the context of our implemented web-based system, which uses examples from molecular biology.-based system, which uses examples from molecular biology.
Date created
2003
DOI
doi:10.7939/R36D5PH6N
License information
Creative Commons Attribution 3.0 Unported
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