Assignment 3
273 Business Intelligence for Analytical Decisions
This assignment must be completed individually. Submit Word file to online drop box on Canvas. Write your name in the Word file.
Q.1. Consider a decision tree (as shown below) for launching new technology products:
The branching probabilities are provided. Given this decision tree, find the probability Pr[Launch = Yes | evidence] in each of the following cases.
(a) Consumer Interest = High, Technology = Emerging
(b) Consumer Interest = High, Unit cost = Low
(c) Technology = Emerging
Q. 2. A winery maintains a dataset containing information about customers who subscribe to its tasting events and special offers for wine cases. The winery occasionally mails tasting samples of new wines in an effort to increase sales. The chief marketing officer is aiming to send samples of a newly produced wine to customers who are NOT likely to place an order for the new wine (when probability of purchase is less than 0.5). Based on a survey conducted amongst its customers and their willingness to buy a case of the new wine before tasting it, the firm collected tree attributes – whether customers prefer dry, or have preference for red wine and their ages. The classification problem is to predict whether customers will buy or not buy wine, with certain confidence/probability. The following classification tree was induced:
Prefer
Dry?
Yes
No
Preference for
Red?
Age
No
Yes
<50
>=50
Buy
Buy
(with probability
Not buy
0.9)
(with probability
with probability
Not Buy
0.6
with probability
0.93
0.95
Answer the following questions:
1) After running a data mining software, suppose the above tree is generated. If you have to choose a single attribute to predict which customer is likely to place an order or not, which attribute would you use (check one and briefly justify your choice)?
A. Preference for red wine
B. Age (whether the customer is older or younger than 50 years old)
C. Preference for dry wine
D. Impossible to determine given the information provided.
2) The Winery manager, Barbara, wants to consult with you if she should send a sample to a new customer named George. She tells you that George prefers dry wine and strongly prefers red wine. What’s your recommendation (on whether to send a sample to George)? Justify. (Note: The chief marketing officer is aiming to send samples of a newly produced wine to customers who are NOT likely to place an order for the new wine (when probability of purchase is less than 0.5).)
3) Assume that the cost of producing and shipping a wine sample to a customer is $8 and the revenue from each order is $100. Assume the company ascertained that the probability of purchase would become 17% for a customer who receives a sample of a new wine. What is the expected incremental revenue and the incremental cost of shipping a sample to customers preferring dry and red wines? Would you suggest shipping a sample to customers preferring dry and red wines? Explain your answer.
Q. 3. An NBA specialist is trying to predict each team’s likelihood of winning the championship (so this is the dependent variable). She decides to use two predictors (i.e. independent variables) – 1) whether a team won more than 55 games during the past season and 2) whether the team as a whole is healthy. The dataset below contains information about the top eight teams and whether the specialist thinks they have a chance to win.
Team
Win less than 55 games?
Team Healthy?
Contender to win the championship?
Phoenix Suns
No
Fair
No
Detroit Pistons
No
Excellent
No
San Antonio Spurs
No
Fair
Yes
Miami Heat
No
Fair
Yes
Denver Nuggets
Yes
Fair
Yes
Seattle Supersonics
Yes
Excellent
No
Houston Rockets
Yes
Excellent
Yes
Dallas Mavericks
No
Excellent
No
Use the examples in the above database to determine which attribute you should split on first, in order to build a decision tree to predict whether a team is a contender to win. Explain each step and show all relevant computations. To simplify your computation, you are given that the information gain if splitting on “team healthy” is 0.189.
You may need the following logarithm values for answering the question:
= -0.585, = -1.585, = -1, = -0.737, = -1.322
= -2, = -0.415
Q. 4. A bank selected 12 inputs to predict whether each applicant defaults. The output variable (BAD) indicates whether an applicant defaulted on the home equity line of credit. The following table describes the variables used.
Name
Type
Description
BAD
Binary
1=applicant defaulted on loan or seriously delinquent
0=applicant paid loan
CLAGE
Numeric
Age of oldest credit line in months
CLNO
Numeric
Number of credit lines
DEBTINC
Numeric
Debt-to-income ratio
DELINQ
Numeric
Number of delinquent credit lines
DEROG
Numeric
Number of major derogatory reports
JOB
Nominal
Occupational categories
LOAN
Numeric
Amount of the loan request
MORTDUE
Numeric
Amount due on existing mortgage
NINQ
Numeric
Number of recent credit inquiries
REASON
Binary
DebtCon=debt consolidation
HomeImp=home improvement
VALUE
Numeric
Value of current property
YOJ
Numeric
Years at present job
Question 4.a: Download the file HMEQ.arff from Canvas. Load HMEQ.arff into WEKA. What’s the class distribution for BAD (i.e. percentage of 1s vs. percentage of 0s)? Build a classification model to predict whether a customer will default using decision tree model J48 (classifiers -> trees -> J48). Please use the default parameter setting for J48, and choose Percentage split (66%) for Test options. Please report the classification accuracy rate of the model built, as well as the confusion matrix. Use confusion matrix to comment on how good the model is.
4.b Now, go back to the Preprocess window where you have the HMEQ.arff file open. Under Filter, choose filters-> supervised->instance->Resample. In the parameter window for Resample, change biasToUniformClass to 1 while leaving other parameters unchanged (i.e. invertSelection=False, noReplacement=False, sampleSizePercent=100). Click on Apply and now report the class distribution of BAD. Use the same model set up in 4.a to build the model. Please report the classification accuracy rate of the model built, as well as the confusion matrix. Please compare with the prior class distribution and comment on how good the model is, and also use confusion matrix to comment on how good the model is, especially compared to the model in 4.a.
2
1
log
2
5
3
log
2
5
2
log
2
4
1
log
2
4
3
log
2
3
2
log
2
3
1
log
2