Workspace Node: Naive Bayes Classifiers - Results - Prior Tab

In the Naive Bayes Classifiers node dialog box, under the Results heading, select the Prior tab to access the following options.

Element Name Description
Threshold When the frequency of a categorical level of an independent variable is zero with respect to one of the categorical levels of a dependent variable, the likelihood of the Naive Bayes model will always yield zero, which is rather superficial. In this field, you can enter a threshold parameter that specifies a lower bound to use when such frequencies are encountered by the Naive Bayes model.
Dependent variable Naive Bayes supports multiple classification tasks. Use this drop-down list to select the categorical variable for which you want to specify prior values.
Default prior probability (calculated from data) Select this option button to assign prior values to the dependent categorical levels on the basis of their relative frequency in the training sample.
Equal (uniform) prior probability Select this option button to assign equal priors to the categorical levels. From a Bayesian point of view, this option implies that you have no expectation as to which class a new data case might belong.
Descriptive statistics Click this button to produce spreadsheets of classification summary and confusion matrix.
Predictions Click this button to create a spreadsheet of the variables and results selected in the Include group box on the Specifications - Quick tab.

Options / C / W. See Common Options.

OK Click the OK button to accept all the specifications made in the dialog box and to close it. The analysis results will be placed in the Reporting Documents node after running (updating) the project.