ISYE 6501 homework 7 solved

Question 10.1
Using the same crime data set as in Questions 8.2 and 9.1, find the best model you can using
(a) a regression tree model, and
(b) a random forest model.
In R, you can use the tree package or the rpart package, and the randomForest package. For
each model, describe one or two qualitative takeaways you get from analyzing the results (i.e., don’t just
stop when you have a good model, but interpret it too).
Question 10.2
Describe a situation or problem from your job, everyday life, current events, etc., for which a logistic
regression model would be appropriate. List some (up to 5) predictors that you might use.
Question 10.3
1. Using the GermanCredit data set germancredit.txt from http://archive.ics.uci.edu/ml/machinelearning-databases/statlog/german / (description at
http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29 ), use logistic
regression to find a good predictive model for whether credit applicants are good credit risks or
not. Show your model (factors used and their coefficients), the software output, and the quality
of fit. You can use the glm function in R. To get a logistic regression (logit) model on data where 
the response is either zero or one, use family=binomial(link=”logit”) in your glm
function call.
2. Because the model gives a result between 0 and 1, it requires setting a threshold probability to
separate between “good” and “bad” answers. In this data set, they estimate that incorrectly
identifying a bad customer as good, is 5 times worse than incorrectly classifying a good
customer as bad. Determine a good threshold probability based on your model.