Explaining Naïve Bayes Classifications
نویسندگان
چکیده
Naïve 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 Naïve 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.
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TCXplain: Transparent Explanation of Naïve Bayes Classifications
Naïve Bayes (NB) classifiers are popular tools for predicting the labels of query instances, after being constructed from a training set. However, many training sets contain noisy data, so a user may be reluctant to blindly trust an NB classifier. TCXplain is a novel graphical explanation facility for NB classifiers. First, it transparently explains the classifier reasoning to foster user confi...
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