Database Reference
In-Depth Information
9)
Run the model. You will see familiar results—the tree remains the same as it was in Figure
10-6, for now. Click on the ExampleSet tab next to the Tree tab. Our tree has been
applied to our scoring data. As was the case with logistic regression, confidence attributes
have been created by RapidMiner, along with a prediction attribute.
Figure 10-9. Meta data for scoring data set predictions.
10)
Switch to Data View using the radio button. We see in Figure 10-10 the prediction for
each customer's adoption group, along with confidence percentages for each prediction.
Unlike the logistic regression example in the previous chapter, there are four confidence
attributes, corresponding to the four possible values in the label (eReader_Adoption). We
interpret these the same way that we did with the other models though—the percentages
add to 100%, and the prediction is whichever category yielded the highest confidence
percentage. RapidMiner is very (but not 100%) convinced that person 77373 (Row 14,
Figure 10-10) is going to be a member of the early majority (88.9%). Despite some
uncertainty, RapidMiner is completely sure that this person is
not
going to be an early
adopter (0%).
