(Solved by Humans)-a) Using the naive rule on the training set, classify a customer

Question

a) Using the naive rule on the training set, classify a customer with the following
characteristics: Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg =
2, Education = 2, Mortgage = 0, Securities Account = 0, CD Account = 0, Online
= 1, CreditCard = 1.
b) Compute the confusion matrix for the validation set based on the naive rule.
c) Perform a k-nearest neighbor classification with all predictors except ID and ZIP
code using k = 1. Remember to transform categorical predictors with more than
two categories into dummy variables first. Specify the success class as 1 (loan
acceptance), and use the default cutoffvalue ofO.5. How would this customerbe
classified?
d) What is a choice of k that balances between overfitting and ignoring the predictor
information?
e) Show the classification matrix for the validation data that results from using the
best k.
t) Classify the customer using the best k.
g) Repartition the data, this time into training, validation, and test sets (50%: 30%:
20%). Apply the k-NN method with the k chosen above. Compare the confusion
matrix of the test set with that of the training and validation sets. Comment on
the differences and their reason.