• 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 14.1
The breast cancer data set breast-cancer-wisconsin.data.txt from
http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/ (description at http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29 ) has missing values.
1. Use the mean/mode imputation method to impute values for the missing data.
2. Use regression to impute values for the missing data.
3. Use regression with perturbation to impute values for the missing data.
4. (Optional) Compare the results and quality of classification models (e.g., SVM, KNN) build using
(1) the data sets from questions 1,2,3;
(2) the data that remains after data points with missing values are removed; and (3) the data set when a binary variable is introduced to indicate missing values.
Question 15.1
Describe a situation or problem from your job, everyday life, current events, etc., for which optimization would be appropriate. What data would you need?
