Importance

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Abstract

A method, system, and computer program product visualizes the structure of an evidence classifier. An evidence inducer generates an evidence classifier based on a training set of labeled records. A mapping module generates visualization data files. An evidence visualization tool uses the visualization data files to display an evidence pane and/or a label probability pane. A first evidence pane display view shows a normalized conditional probability of each label value, for each attribute value. The first evidence pane display view can be a plurality of rows of pie charts. Each pie slice in a pie chart has a size which is a function of the normalized conditional probability of each label value for the respective attribute value. A second evidence pane display view shows relative conditional probabilities of a selected label value, for each attribute value. The second evidence pane display view can be a plurality of rows of bars. Bar height is a function of a conditional probability of a respective attribute value conditioned on the selected label value. A first label probability pane display view shows a pie chart of prior probabilities of each label value based on the training set. A second label probability pane display view shows a pie chart of posterior probabilities of each label value based on at least one selected attribute value. An importance slider controls filtering of attributes based on the importance of the attributes to a classification of unlabeled records. A count slider filters out attribute values having relatively low record counts. The evidence classifier visualization tool further provides sorting of attributes and/or attribute values. A subtracting minimum evidence capability is provided.

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First Claim