• Every learner should submit his/her own homework solutions. However, you are allowed to discuss the homework with each other (in fact, I encourage you to form groups and/or use the forums) – but everyone must submit his/her own solution; you may not copy someone else’s solution.
• The homework will be peer-graded. In analytics modeling, there are often lots of different approaches that work well, and I want you to see not just your own, but also others.
• The homework grading scale reflects the fact that the primary purpose of homework is learning:
Rating
Meaning
Point value (out of 100)
4
All correct (perhaps except a few details) with a deeper solution than expected
100
3
Most or all correct
90
2
Not correct, but a reasonable attempt
75
1
Not correct, insufficient effort
50
0
Not submitted
0
Question 10.1
Using the same crime data set uscrime.txt 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/machine-learning-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.
