# 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.

![](https://help.qlik.com/talend/en-US/components/8.0/Resources/images/developing_a_machine_learning_routine_with_talend_and_spark_32.png)

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)