1. Decision Tree (Rule Induction)
Analysis of customer behavior and service modeling
Decision Tree (Rule Induction)

2. Poll: Which data mining technique..?

3. Classification Process with 10 records Step 1: Model Construction with 6 records
Algorithms
Training
Data
Classifier
(Model)
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’

4. Step 2: Test model with 6 records & Use the Model in Prediction
Classifier
Testing
Data
Unseen Data
(Jeff, Professor, 4)
Tenured?

5. Who buys notebook computer? Training Dataset is given below:
This follows an example from Quinlan’s ID3

6. Tree Output: A Decision Tree for Credit Approval
age?
<=30
overcast
30..40
>40
student?
yes
credit rating? no
yes
excellent
fair no
yes
yes no

7. Extracting Classification Rules from Trees
Represent the knowledge in the form of IF-THEN rules
One rule is created for each path from the root to a leaf
Each attribute-value pair along a path forms a conjunction
The leaf node holds the class prediction
Rules are easier for humans to understand
Example
IF age = “<=30” AND student = “no” THEN buys_computer = “no”
IF age = “<=30” AND student = “yes” THEN buys_computer = “yes”
IF age = “31…40” THEN buys_computer = “yes”
IF age = “>40” AND credit_rating = “excellent” THEN buys_computer = “yes”
IF age = “>40” AND credit_rating = “fair” THEN buys_computer = “no”

8. An Example of ‘Car Buyers’ – Who buys Lexton?no
Job
M/F
Area
Age
Y/N 1 NJ M N 35 2 F 51 3 OW 31 Y 4 EM 38 5 S 33 6 54 7 49 8 32 9 10 11 12 50 13 36 14
* (a,b,c) means
a: total # of records, b: ‘N’ counts, c: ‘Y’ counts

9. Lab on Decision Tree(1) SPSS Clementine, SAS Enterprise Miner
See5/C5.0Download See5/C
Evaluation from

10. Lab on Decision Tree(2)
From below initial screen, choose File – Locate Data

11. Lab on Decision Tree(3)
Select housing.data from Samples folder and click open.

12. Lab on Decision Tree(3(4)
This data set is on deciding house price in Boston area. It has 350 cases and 13 variables.

13. Lab on Decision Tree (5) Input variables crime rate
proportion large lots: residential space
proportion industrial: ratio of commercial area
CHAS: dummy variable
nitric oxides ppm: polution rate in ppm
av rooms per dwelling: # of room for dwelling
proportion pre-1940
distance to employment centers: distance to the center of city
accessibility to radial highways: accessibility to high way
property tax rate per $10\,000
pupil-teacher ratio: teachers’ rate
B: racial statistics
percentage low income earners: ratio of low income people
Decision variable
Top 20%, Bottom 80%

14. Lab on Decision Tree(6)
For the analysis, click Construct Classifier or click Construct Classifier from File menu

15. Lab on Decision Tree(7)
Click on Global pruning to (V ). Then, click OK

16. Lab on Decision Tree(8) Decision Tree Evaluation with Training data
Evaluation with Test data

17. Lab on Decision Tree(9) Understanding picture
We can see that (av rooms per dwelling) is the most important variable in deciding house price.

18. Lab on Decision Tree(11) 의사결정나무 그림으로는 규칙을 알아보기 어렵다.
To view the rules, close current screen and click Construct Classifier again or click Construct Classifier from File menu.

19. Lab on Decision Tree(12)
Choose/click Rulesets. Then click OK.

20. Lab on Decision Tree(13)