Interpreting the Output
The outcome of training and testing appears in the Classifier Output box on the right.
Scroll through the text and examine it. First, look at the part that describes the decision
tree, reproduced in Figure 17.2(b). This represents the decision tree that was
built, including the number of instances that fall under each leaf. The textual representation
is clumsy to interpret, but Weka can generate an equivalent graphical version.
Here’s how to get the graphical tree. Each time the Start button is pressed and
a new classifier is built and evaluated, a new entry appears in the Result List panel
in the lower left corner of Figure 17.2(a). To see the tree, right-click on the entry
trees.J48 that has just been added to the result list and choose Visualize tree. A
window pops up that shows the decision tree in the form illustrated in Figure 17.3.
Right-click a blank spot in this window to bring up a new menu enabling you to
auto-scale the view. You can pan around by dragging the mouse.
Now look at the rest of the information in the Classifier Output area. The next
two parts of the output report on the quality of the classification model based on the
chosen test option.
This text states how many and what proportion of test instances have been
correctly classified:
Correctly Classified Instances 14 100%
This is the accuracy of the model on the data used for testing. In this case it is
completely accurate (100%), which is often the case when the training set is used
for testing.
At the bottom of the output is the confusion matrix: