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The file “dt_train.csv” contains 601 lines with 10 variables. The first line contains column headers that may be interpreted as follows:
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id: observation identifier.
t1: measurement on test 1; t2: measurement on test 2.
t3: measurement on test 3; t4: measurement on test 4.
t5: measurement on test 5; t6: measurement on test 6.
t7: measurement on test 7; t8: measurement on test 8.
d: binary output variable set to 1 if product is defective and 0 otherwise.
The next 600 lines contain 600 examples, for which the values of the above features are specified.
The table below reproduces the first 2 observations.
id
t1
t2
t3
t4
t5
t6
t7
t8
d
1
17
3
31
54
66
54
45
84
1
2
2
15
6
5
82
54
59
87
1
· Use rpart with the training examples to come up with a small set of rules that correctly classify the output variable “d” based on input variable values (t1, t2, t3, t4, t5, t6, t7, and t8).
· Specify the rules.
· The file “dt_test.csv” contains 200 test examples with the same 10 variables. Test your trained classifier on these test example and present your confusion matrix. Comment on your classification accuracy.
· Then use the rules to predict the output class d for the following test cases (presented in the file “dt_new.csv”):
new_case
t1
t2
t3
t4
t5
t6
t7
t8
d
1
8
86
55
53
36
12
82
19
2
22
36
80
69
90
33
22
6
3
74
26
32
26
38
52
63
12
4
66
71
71
52
42
88
89
70
5
55
72
61
41
91
39
50
96
6
34
58
22
84
84
61
95
57
7
23
70
39
65
16
71
96
78
8
9
19
67
43
2
20
92
3
9
6
71
20
6
27
58
6
22
10
68
40
86
82
82
44
61
48
