Select a cell on the Data_Partition worksheet, then on the XLMiner ribbon, from the Data Mining tab, select Classify - Naïve Bayes to open the Naïve Bayes - Step 1 of 3 dialog.įrom the Selected Variables list, select Var2, Var3, Var4, Var5, and Var6, and at Output Variable, select TestRest/Var1. The # Classes statistic is automatically updated with a value of 2 when the Output Variable is selected, indicating that the Output Variable contains two classes, 0 and 1.Ĭhoose the value that will be the indicator of Success by clicking the down arrow next to Specify “Success” class (for Lift Chart). On the XLMiner ribbon, from the Data Mining tab, select Classify - Partition - Standard Partition to open the Standard Data Partition dialog. For more information on partitioning a data set, see the Data Mining Partition section. A portion of the data set appears below.įirst, partition the data into Training and Validation Sets using the Standard Data Partition defaults of 60% of the data randomly allocated to the Training Set and 40% of the data randomly allocated to the Validation Set. On the XLMiner ribbon, from the Applying Your Model tab, click Help - Examples, then Forecasting/Data Mining Examples to open the Flying_Fitness.xlsx example data set.
The following example illustrates XLMiner's Naïve Bayes classification method.
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