Database Reference
In-Depth Information
REVIEW QUESTIONS
1)
Where do neural networks get their name? What characteristics of the model make it
'neural'?
2)
Find another observation in this chapter's example that is interesting but not obvious,
similar to the Lance Goodwin observation. Why is the observation you found interesting?
Why is it less obvious than some?
3)
How should confidence percentages be used in conjunction with a neural network's
predictions?
4)
Why might a data miner prefer a neural network over a decision tree?
5)
If you want to see a node's details in a RapidMiner graph of a neural network, what can
you do?
EXERCISE
For this chapter's exercise, you will create a neural network to predict risk levels for loan applicants
at a bank. Complete the following steps.
1)
Access the companion web site for this text. Locate and download the training data set
labeled Chapter11Exercise_TrainingData.csv.
2)
Import the training data set into your RapidMiner repository and name it descriptively.
Drag and drop the data set into a new, blank main process.
3)
Set the Credit_Risk attribute as your label. Remember that Applicant_ID is not predictive.
4)
Add a Neural Net operator to your model.
