Interpreting results

In the last section, we took a look at the theoretical underpinnings of decision trees and took a conceptual tour of how they are constructed. In this section, we will return to the classification example we examined earlier in the chapter and take a closer look at the format of the results as well as how to interpret them.

Earlier in the chapter, we created a trained model to predict whether a given breast tissue sample was malicious or benign (as a reminder, in this dataset malignant is denoted by class 2 and benign by class 4). A snippet of the classification results for this model is shown in Figure 11.18.

Figure 11.17 – Classification results for a sample data point in the Wisconsin breast cancer dataset

With this trained model, we can take previously unseen data points and make predictions. What form do these predictions take? In the simplest form, a data point is assigned a class label (the field ml.Class_prediction in...