Consider the data in Table above (end of chapter 6 or 8). The target variable is salary. Start by discretizing salary and age as follows:
Less than $35,000 Level 1
$35,000 to less than $45,000 Level 2
$45,000 to less than $55,000 Level 3
Above $55,000 Level 4
0 – 30 <= 30
31 - 40 <= 40
Above 40 <= 50
5.1 Construct a classification and regression tree to classify salary based on the other variables only one split level.
Hint: you may want to set up the excel file like the following
Split
PL
PR
Level
P( j |tL )
P( j |tR)
2PL PR
Q(s|t)
Φ(s|t)
1
0.273
0.727
L1
0.333
0.125
0.397
0.583
0.231
L2
0.333
0.250
L3
0.333
0.375
L4
0.000
0.250
2
5.2
Use these categorized features to answer the following questions.
Important: make sure your categories are represented by the “factor” data type in R and DO NOT replace the missing values.
Features Domain
-- -----------------------------------------
Sample code number id number
F1. Clump Thickness 1 - 10
F2. Uniformity of Cell Size 1 - 10
F3. Uniformity of Cell Shape 1 - 10
F4. Marginal Adhesion 1 - 10
F5. Single Epithelial Cell Size 1 - 10
F6. Bare Nuclei 1 - 10
F7. Bland Chromatin 1 - 10
F8. Normal Nucleoli 1 - 10
F9. Mitoses 1 - 10
Diagnosis Class: (2 for benign, 4 for malignant
5.2
Use the CART methodology to develop a classification model for the Diagnosis.
