Evaluating your decision tree performance

This section explains how to evaluate the results of your decision tree.

Below is a confusion matrix using the data from your test Job.

The model tries to predict (conversion = no) as being either true of false.

• TN = 15
• TP = 446
• FN = 12
• FP = 41
• Accuracy = (TP+TN)/Total = (15+446)/(446+15+12+41) = .90
• Sensitivity = TP/(TP+FN) = (446)/(446+12) = .97
• Specificity = TN/(TN+FP) = (15)/(15+41) = .27

When you tested the tree model:

• It was correct 90% of the time (accuracy)
• It accurately predicted 97% of those persons who did not result in a conversion (sensitivity)
• It accurately predicted 27% of those persons who did result in a conversion (specificity)