On the XLMiner ribbon, from the Data Mining tab, select Classify - Naive Bayes to open the Naive Bayes - Step 1 of 3 dialog. Following are descriptions of the options available from the three Naive Bayes dialogs.

Naive Bayes - Step 1 of 3 Dialog

Variables In Input Data

The variables included in the data set appear here.

Selected Variables

Variables selected to be included in the output appear here.

Output Variable

The dependent variable or the variable to be classified appears here.

# Classes

Displays the number of classes in the Output variable.

Specify "Success" class (for Lift Chart)

This option is selected by default. Click the drop-down arrow to select the value to specify a success. This option is enabled when the number of classes for the output variable is equal to 2.

Specify initial cutoff probability for success

Enter a value between 0 and 1 here to denote the cutoff probability for success. If the calculated probability for success for an observation is greater than or equal to this value, than a success (1) will be predicted for that observation. If the calculated probability for success for an observation is less than this value, then a non-success (0) will be predicted for that observation. The default value is 0.5. This option is enabled when the number of classes for the output variable is equal to 2.

Naive Bayes - Step 2 of 3 Dialog

According to relative occurrences in training data

When this option is selected, XLMiner calculates the class probabilities from the training data. For the first class, XLMiner calculates the probability using the number of 0 records/total number of points. For the second class, XLMiner calculates the probability using the number of 1 records/total number of points.

Use equal prior probabilities

When this option is selected, XLMiner uses 0.5 probability for both classes.

User specified prior probabilities

Select this option to manually enter the desired class and probability value.

Partitioning Options

XLMiner V2015 provides the ability to partition a data set from within a classification or prediction method by selecting Partitioning Options on the Step 2 of 3 dialog. If this option is selected, XLMiner partitions the data set immediately before running the prediction method. If partitioning has already occurred on the data set, this option is disabled. For more information on partitioning, see the Data Mining Partition section.

Naive Bayes - Step 3 of 3 Dialog

Score Training Data

Select these options to show an assessment of the performance of the tree in classifying the Training Set. The report is displayed according to the specifications: Detailed, Summary, and Lift Charts. Lift Charts are only available when the Output Variable has two classes.

Score Validation Data

These options are enabled when a Validation Set is present. Select these options to show an assessment of the performance of the tree in classifying the Validation Data. The report is displayed according to your specifications - Detailed, Summary, and Lift charts. Lift charts are only available when the Output Variable has two classes.

Score Test Data

These options are enabled when a test data set is present. Select these options to show an assessment of the performance of the tree in classifying the test data. The report is displayed according to the specifications: Detailed, Summary, and Lift Charts. Lift Charts are only available when the Output Variable has two classes.

Score New Data

See the Scoring New Data section for information on the Score New Data options.