Homework 4: Decision Tree Inductive Learning

The file “dt_train.csv” contains 601 lines with 10 variables. The first line contains column headers that may be interpreted as follows:

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

 

 

Field of study: 
Date Due: 
Sunday, November 11, 2018

Answer

Decision Tree Inductive Learning

Homework 4: Decision Tree Inductive Learning

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