In the Enterprise Miner Reference Help, Model Comparison node chapter, the section Comparing Models with Binary Targets gives a nice chart showing what exactly these rates represent (shown below, but not formatted well). It mentions that typically the value 1 is treated as the target event, and 0 as the non-event. This is controlled by the Order that is set for the target variable in the Input Data node or a Metadata node. For a binary target, the order by default is Descending, which means the first value when sorting the target levels in descending order is used as the event (hence 1 if values are 0 and 1). Hope that helps...
Classification Measures
Binary targets are targets whose states can be classified as event and non-event. Typically, event is associated with a value of 1 for the target variable, and non-event is associated with a value of 0 for the target variable. The following table describes the predictive relationships between event and non-event.
Predicted Non-Event
Predicted Event
Total Actual Probability
Non-Event
A (true negative)
B (false positive)
(A + B) / (A + B + C + D )
Event
C (false negative)
D (true positive)
(C + D) / (A + B + C + D )
Total Predicted
A + C
B + D
A + B + C + D
The following classification measures are derived from the relationships in the table:
Classification (Accuracy) Rate: 100 * (A + D) / (A + B + C + D)
Misclassification Rate: 100 * (1 - ((A + D) / (A + B + C + D)))
Sensitivity (True Positive Rate): 100 * D / (C + D)
Specificity (True Negative Rate): 100 * A / (A + B)
1 – Specificity (False Positive Rate): 100 * B / (A + B)
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