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Data mining Xlminer

1 min read
Posted on 
October 31st, 2022
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Codelist

Codelist

.  

Categorical

: < 0 DM

0 DM

DM

: no checking account

.  

Categorical

.  

.

Purpose of credit Binary

.

Purpose of credit Binary

.

Purpose of credit Binary

.

Purpose of credit Binary

.

Purpose of credit Binary

.

Numerical

.

Categorical

DM

0 DM

<= ... <

DM

.

Categorical

.

Numerical

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

Categorical

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

.

Numerical

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

.

Numerical

.

Categorical

.

Numerical

.

Binary 0: No, 1: Yes

.

Binary 0: No, 1: Yes

Binary 0: No, 1: Yes

Var. # Variable Name Description Variable Type Code Description
1 OBS# Observation No. Categorical
2.   CHK_ACCT Checking account status 0
1: 0 < ...< 20
2 : =>

200
3
3.   DURATION Duration of credit in months Numerical
4 HISTORY Credit history 0: no credits taken
1: all credits at this bank paid back duly
2: existing credits paid back duly till now
3: delay in paying off in the past
4: critical account
5 NEW_CAR Purpose of credit Binary car (new)

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0: No, 1: Yes
6 USED_CAR car (used) 0: No, 1: Yes
7 FURNITURE furniture/equipment 0: No, 1: Yes
8 RADIO/TV radio/television 0: No, 1: Yes
9 EDUCATION education 0: No, 1: Yes
10 RETRAINING retraining 0: No, 1: Yes
11 AMOUNT Credit amount
12 SAV_ACCT Average balance in savings account 0 : < 100
1 : 100<= ... < 50
2 :

500 1000
3 : =>1000 DM
4 : unknown/ no savings account
13 EMPLOYMENT Present employment since 0 : unemployed
1: < 1 year
2 : 1 <= ... < 4 years
3 : 4 <=... < 7 years
4 : >= 7 years
14 INSTALL_RATE Installment rate as % of disposable income
15 MALE_DIV Applicant is male and divorced
16 MALE_SINGLE Applicant is male and single
17 MALE_MAR_WID Applicant is male and married or a widower
18 CO-APPLICANT Application has a co-applicant
19 GUARANTOR Applicant has a guarantor
20. PRESENT_RESIDENT Present resident since – years 0: <= 1 year
1<…<=2 years
2<…<=3 years
3:>4years
21 REAL_ESTATE Applicant owns real estate
22 PROP_UNKN_NONE Applicant owns no property (or unknown)
23 AGE Age in years
24 OTHER_INSTALL Applicant has other installment plan credit
25 RENT Applicant rents
26 OWN_RES Applicant owns residence
27 NUM_CREDITS Number of existing credits at this bank
28 JOB Nature of job 0 : unemployed/ unskilled – non-resident
1 : unskilled – resident
2 : skilled employee / official
3 : management/ self-employed/highly qualified employee/ officer
29 NUM_DEPENDENTS Number of people for whom liable to provide maintenance
30 TELEPHONE Applicant has phone in his or her name
31 FOREIGN Foreign worker
32 RESPONSE Credit rating is good

Data

OBS# CHK_ACCT DURATION HISTORY NEW_CAR USED_CAR FURNITURE RADIO/TV EDUCATION RETRAINING AMOUNT SAV_ACCT EMPLOYMENT INSTALL_RATE MALE_DIV MALE_SINGLE

CO-APPLICANT GUARANTOR PRESENT_RESIDENT REAL_ESTATE PROP_UNKN_NONE AGE OTHER_INSTALL RENT OWN_RES NUM_CREDITS JOB NUM_DEPENDENTS TELEPHONE FOREIGN RESPONSE

1 0 6 4 0 0 0 1 0 0

4 4 4 0 1 0 0 0 4 1 0

0 0 1 2 2 1 1 0 1

2 1

2 0 0 0 1 0 0

0 2 2 0 0 0 0 0 2 1 0 22 0 0 1 1 2 1 0 0 0

3 3 12 4 0 0 0 0 1 0

0 3 2 0 1 0 0 0 3 1 0

0 0 1 1 1 2 0 0 1

4 0

2 0 0 1 0 0 0

0 3 2 0 1 0 0 1 4 0 0

0 0 0 1 2 2 0 0 1

5 0 24 3 1 0 0 0 0 0

0 2 3 0 1 0 0 0 4 0 1

0 0 0 2 2 2 0 0 0

6 3

2 0 0 0 0 1 0

4 2 2 0 1 0 0 0 4 0 1

0 0 0 1 1 2 1 0 1

7 3 24 2 0 0 1 0 0 0

5

2 4 3 0 1 0 0 0 4 0 0 53 0 0 1 1 2 1 0 0 1

8 1 36 2 0 1 0 0 0 0

8

0 2 2 0 1 0 0 0 2 0 0 35 0 1 0 1 3 1 1 0 1

9 3 12 2 0 0 0 1 0 0

9

3 3 2 1 0 0 0 0 4 1 0

0 0 1 1 1 1 0 0 1

10 1 30 4 1 0 0 0 0 0

0 0 4 0 0 1 0 0 2 0 0 28 0 0 1 2 3 1 0 0 0

11 1 12 2 1 0 0 0 0 0

0 1 3 0 0 0 0 0 1 0 0 25 0 1 0 1 2 1 0 0 0

12 0 48 2 0 0 0 0 0 1

08

0 1 3 0 0 0 0 0 4 0 0 24 0 1 0 1 2 1 0 0 0

13 1 12 2 0 0 0 1 0 0

7

0 2 1 0 0 0 0 0 1 0 0 22 0 0 1 1 2 1 1 0 1

14 0 24 4 1 0 0 0 0 0

0 4 4 0 1 0 0 0 4 0 0

0 0 1 2 1 1 0 0 0

15 0 15 2 1 0 0 0 0 0

3

0 2 2 0 0 0 0 0 4 0 0 28 0 1 0 1 2 1 0 0 1

16 0 24 2 0 0 0 1 0 0

2

1 2 4 0 0 0 0 0 2 0 0 32 0 0 1 1 1 1 0 0 0

17 3 24 4 0 0 0 1 0 0

4

4 4 4 0 1 0 0 0 4 0 0 53 0 0 1 2 2 1 0 0 1

18 0 30 0 0 0 0 0 0 1

4 1 2 0 1 0 0 0 3 0 0 25 1 0 1 3 2 1 0 0 1

19 1 24 2 0 1 0 0 0 0

9

0 4 4 0 0 0 0 0 2 0 1

0 0 0 1 3 1 1 0 0

20 3 24 2 0 0 0 1 0 0

0

2 4 3 0 1 0 0 0 2 0 0 31 0 0 1 1 2 2 1 0 1

21 3 9 4 1 0 0 0 0 0

0 2 4 0 1 0 0 0 4 0 0 48 0 0 1 3 2 1 1 0 1

22 0 6 2 0 0 0 1 0 0

2 2 2 0 1 0 0 0 3 1 0 44 0 1 0 1 2 2 0 0 1

23 0 10 4 1 0 0 0 0 0

0 1 1 0 1 0 0 0 3 1 0 48 0 1 0 2 1 2 0 1 1

24 1 12 4 0 1 0 0 0 0

4

1 1 3 0 1 0 0 0 4 0 0 44 0 0 1 1 2 1 0 0 1

25 3 10 4 0 0 1 0 0 0

9

4 2 2 0 0 1 0 0 1 0 0 26 0 0 1 2 2 1 0 1 1

26 0 6 2 0 0 1 0 0 0

4

0 2 1 0 1 0 0 0 2 1 0 36 1 0 1 1 1 1 1 0 1

27 3 6 0 0 0 0 1 0 0

0 4 4 0 0 1 0 0 4 0 0

0 0 1 1 1 1 0 0 1

28 2 12 1 0 0 0 1 0 0

3 2 3 0 0 0 0 0 3 1 0 42 0 1 0 2 2 1 0 0 1

29 1 7 2 0 0 0 1 0 0

5

0 2 3 0 1 0 0 1 2 1 0 34 0 0 1 1 2 1 0 0 1

30 0 60 3 0 0 0 0 0 1

36

0 4 3 0 1 0 0 0 4 0 1

0 0 1 2 2 1 1 0 0

31 1 18 2 0 0 0 0 0 1

3

3 1 3 0 0 1 0 0 3 1 0 36 1 0 1 1 2 1 1 0 1

32 0 24 2 0 0 1 0 0 0

0

0 2 2 0 1 0 0 0 2 0 0 27 1 0 1 1 2 1 0 0 1

1 18 2 1 0 0 0 0 0

1 2 2 0 1 0 0 0 2 0 0 30 0 0 1 2 2 1 1 0 1

34 3 12 4 0 0 0 0 0 1

4 4 4 0 1 0 0 0 4 0 1 57 0 1 0 1 1 1 0 0 1

35 2 12 2 0 0 1 0 0 0

0 1 4 0 0 0 0 0 1 0 0 33 1 0 1 1 3 1 1 0 1

36 1 45 4 0 0 0 1 0 0

0 1 4 0 1 0 0 0 2 0 0 25 0 0 1 2 1 1 0 0 0

37 3 48 4 0 0 0 0 1 0

0 2 1 0 1 0 0 0 3 0 1 31 1 0 0 1 2 1 1 0 1

2 18 2 0 0 0 1 0 0

0

0 2 4 0 1 0 1 0 2 1 0 37 1 0 1 1 2 1 0 0 0

39 2 10 2 0 0 0 0 0 0

5

0 2 2 0 1 0 0 0 2 0 0 37 0 0 1 1 2 1 1 0 1

40 1 9 2 0 0 0 1 0 0

0 2 4 0 1 0 0 0 3 1 0 24 0 0 1 1 2 1 0 0 1

41 3 30 2 0 0 0 1 0 0

3

2 4 4 0 1 0 0 0 2 0 0 30 1 0 1 1 3 1 0 0 1

42 1 12 2 0 0 0 1 0 0

8

2 2 3 1 0 0 0 0 1 0 0 26 0 0 1 1 2 1 1 0 1

43 1 18 3 0 0 0 0 0 0

04

0 2 2 0 1 0 0 0 4 1 0 44 0 0 1 1 1 2 1 0 1

44 0 30 4 0 1 0 0 0 0

1 3 1 0 0 1 0 0 4 0 0 24 0 1 0 2 2 1 0 0 1

45 0 48 4 0 1 0 0 0 0

0 4 4 0 0 0 0 0 4 0 1 58 1 0 0 2 1 1 0 0 0

46 3 11 4 1 0 0 0 0 0

0 1 4 0 0 0 0 0 4 0 0 35 0 0 1 2 3 1 0 0 1

47 3 36 2 0 0 0 1 0 0

9

2 4 4 0 1 0 0 0 4 0 0 39 0 0 1 1 2 1 0 0 1

48 0 6 2 0 1 0 0 0 0

2

2 0 1 0 0 0 0 0 2 0 0 23 0 1 0 1 0 1 1 0 1

49 3 11 4 1 0 0 0 0 0

0 2 1 0 1 0 0 0 4 0 0 39 0 0 1 2 1 1 0 0 1

50 3 12 2 0 0 0 1 0 0

1 2 4 0 0 0 1 0 2 1 0 28 0 0 1 1 2 1 0 0 1

51 1 24 3 0 0 1 0 0 0 2

4 1 4 0 1 0 0 0 2 0 0 29 1 0 1 1 1 1 0 0 1

52 1 27 3 0 1 0 0 0 0

0 4 1 0 1 0 0 0 2 0 0 30 0 0 1 2 3 1 1 0 1

53 3 12 2 0 0 0 1 0 0

2

0 2 3 0 1 0 0 0 2 0 0 25 0 0 1 1 2 1 0 0 1

3 18 2 0 1 0 0 0 0

8

4 2 2 0 1 0 0 0 1 0 0 31 0 0 1 1 2 1 1 0 1

55 1 36 3 1 0 0 0 0 0

5

0 4 4 0 1 0 0 0 4 0 1 57 1 0 0 2 2 1 1 0 0

56 3 6 1 1 0 0 0 0 0

4 2 1 0 1 0 0 1 2 1 0 26 1 0 1 1 1 2 0 0 1

57 1 12 2 0 0 0 1 0 0

4 0 2 0 1 0 0 0 1 0 1 52 0 0 1 1 3 1 1 0 0

58 3 36 4 0 0 0 1 0 0

0 2 2 0 0 0 0 0 2 0 0 31 1 0 1 2 2 1 0 0 1

59 2 18 2 1 0 0 0 0 0

1

0 4 3 0 0 0 0 0 2 0 0 23 0 0 1 1 3 1 0 0 1

60 0 36 4 0 0 1 0 0 0

9

0 1 4 0 0 0 1 0 4 0 1 23 0 1 0 2 1 1 1 0 0

61 1 9 2 0 0 0 0 0 1

1

0 2 2 0 0 1 0 0 1 1 0 27 1 0 1 1 2 1 1 0 1

62 1 15 4 0 0 0 1 0 0

7

4 4 4 0 1 0 0 1 4 1 0 50 0 0 1 2 2 1 1 0 1

63 1 36 0 0 0 0 0 0 1

3

0 4 4 0 1 0 0 0 4 0 1 61 0 0 0 1 3 1 1 0 0

64 1 48 0 0 0 0 0 0 1

21

0 2 2 0 1 0 0 0 2 0 0 25 0 0 1 1 2 1 1 0 0

65 3 24 2 0 0 0 1 0 0

0 1 4 0 0 0 0 0 4 0 0 26 0 0 1 1 2 1 1 0 1

66 3 27 2 0 0 0 0 0 0

4 4 4 0 1 0 0 0 4 0 0 48 0 0 1 4 2 2 1 0 1

67 3 12 2 0 0 0 1 0 0

0 1 2 0 0 0 0 0 2 0 0 29 1 0 1 1 2 1 0 0 1

68 1 12 2 1 0 0 0 0 0

3 2 4 0 0 1 0 0 1 1 0 22 0 0 1 1 2 1 0 0 1

69 3 36 2 0 0 0 0 1 0

9

0 2 4 0 1 0 0 0 4 0 1 37 1 0 0 1 2 1 1 0 0

70 3 36 2 0 0 0 1 0 0

4

4 2 4 0 0 0 0 0 4 0 0 25 0 0 1 1 2 1 0 0 1

71 3 36 2 0 1 0 0 0 0

0 2 1 0 0 0 0 0 2 0 0 30 1 0 1 1 2 1 0 0 1

72 3 7 4 0 0 0 1 0 0

4 4 4 0 1 0 0 0 2 0 0 46 0 1 0 2 1 1 1 0 1

73 0 8 4 0 0 0 0 0 0

4

0 4 3 0 1 0 0 0 4 0 1 51 1 0 0 2 3 2 1 0 1

74 1 42 4 0 0 0 0 0 1

4

0 3 2 0 0 0 0 0 1 1 0 41 1 0 1 2 1 1 0 0 1

0 36 2 0 0 0 0 1 0

4 4 4 0 1 0 0 0 4 0 1 40 0 0 1 1 3 1 1 0 0

0 12 4 0 1 0 0 0 0

6

0 4 4 0 1 0 0 0 4 0 1 66 0 0 0 2 3 1 0 0 1

77 0 42 2 0 0 0 1 0 0

5

0 1 4 0 1 0 0 0 3 0 0 34 0 0 1 1 2 1 0 0 0

78 1 11 3 0 0 0 1 0 0

1

0 3 2 0 1 0 0 0 4 0 0 51 0 0 1 1 2 1 0 0 1

3 54 0 0 1 0 0 0 0

4 2 2 0 1 0 0 0 2 0 0 39 0 0 1 1 1 2 0 0 1

80 1 30 2 0 0 1 0 0 0

2

0 1 2 0 0 1 0 0 1 0 0 22 0 0 1 1 2 1 0 0 1

81 3 24 2 0 0 0 1 0 0

3

4 1 1 0 0 0 0 0 1 0 0 44 0 0 1 2 2 1 1 0 0

82 3 15 2 0 0 0 1 0 0

3

2 4 4 0 1 0 0 0 3 0 0 47 1 0 1 1 2 1 1 0 1

83 3 18 2 0 0 0 0 0 1

8

1 2 3 0 0 0 0 0 4 0 0 24 0 1 0 1 1 1 0 0 1

0 24 2 0 0 0 0 0 0

5

0 4 4 0 0 0 0 1 4 1 0 58 0 0 1 1 1 1 1 0 1

0 10 2 0 0 0 1 0 0

5

0 4 3 0 1 0 0 0 4 1 0 52 0 0 1 1 1 1 0 0 1

3 12 4 0 0 0 0 0 1

2

0 2 4 0 0 0 0 1 2 1 0 29 0 0 1 2 3 1 1 0 1

87 1 18 4 0 0 1 0 0 0

5 0 1 4 0 0 0 0 0 1 0 0 27 0 0 1 2 2 1 0 0 1

1 36 2 0 0 0 0 1 0

2

1 2 1 0 1 0 0 0 4 0 1 47 0 0 0 1 2 2 1 0 0

0 18 2 1 0 0 0 0 0

9

1 3 4 0 1 0 0 0 3 0 0 30 0 0 1 1 3 2 1 0 1

90 0 12 0 0 0 0 0 0 0

0 3 4 0 1 0 0 0 3 1 0 28 0 0 1 2 2 1 0 0 0

91 3 12 4 0 0 0 1 0 0

0 4 4 0 1 0 0 0 4 1 0 56 0 0 1 1 2 1 0 0 1

0 12 4 0 1 0 0 0 0

9

0 4 4 0 1 0 0 0 3 1 0 54 0 0 1 1 2 1 0 0 1

93 3 12 4 0 0 0 1 0 0

4 4 4 0 0 0 0 0 3 0 0 33 1 0 1 1 1 2 0 0 0

94 2 24 4 0 0 1 0 0 0

7

4 4 4 0 1 0 1 0 4 0 1 20 0 1 0 2 2 1 0 0 1

95 1 12 2 1 0 0 0 0 0

8

3 4 4 0 1 0 0 0 4 1 0 54 0 0 1 1 2 1 1 0 1

96 1 54 0 0 0 0 0 0 1

45

0 1 3 0 1 0 0 0 4 0 1 58 0 1 0 1 2 1 1 0 0

97 3 12 4 0 0 0 0 1 0

2

4 3 4 0 0 0 0 0 2 0 0 61 0 0 1 1 2 1 0 0 1

1 18 2 0 0 0 0 0 1

2

1 2 4 0 1 0 0 0 4 0 0 34 0 0 1 1 2 1 0 0 1

99 1 36 4 0 0 0 1 0 0

0 4 4 0 1 0 0 0 4 1 0 36 0 0 1 1 2 1 0 0 1

100 1 20 3 0 1 0 0 0 0

7

4 3 3 0 1 0 0 0 4 0 0 36 1 1 0 2 3 2 1 0 1

3 24 2 1 0 0 0 0 0

9

1 4 4 0 0 1 0 0 4 1 0 41 0 1 0 1 1 1 0 0 1

1 36 2 0 0 0 1 0 0

3

0 3 4 0 1 0 0 0 4 0 0 24 0 1 0 1 2 1 0 0 1

3 6 3 0 0 0 1 0 0

0 2 3 0 0 0 0 0 2 1 0 24 0 0 1 1 2 1 0 0 1

1 9 4 0 0 1 0 0 0

9

0 3 4 0 1 0 0 0 3 0 0 35 0 1 0 1 2 1 1 0 1

3 12 2 0 1 0 0 0 0

5

4 1 2 0 0 1 0 0 4 0 0 26 0 1 0 1 2 1 1 0 1

1 24 4 0 0 0 0 0 0

38

0 2 2 0 1 0 1 0 3 0 0 39 0 0 1 2 3 2 1 0 0

3 18 1 1 0 0 0 0 0

8

0 4 2 0 1 0 0 0 4 0 1 39 1 0 1 2 3 2 1 0 0

108 1 12 2 1 0 0 0 0 0

8

0 3 2 0 1 0 0 0 2 0 0 32 0 0 1 1 2 1 0 0 1

0 24 2 0 0 1 0 0 0

4 1 1 0 0 0 0 0 2 0 0 30 0 0 1 1 2 1 1 1 1

110 1 14 2 0 0 0 0 0 1

2 4 1 0 0 1 0 0 2 1 0 35 0 0 1 1 2 1 1 0 1

1 6 3 0 0 0 0 0 1

1 4 1 1 0 0 0 0 2 0 0 31 1 0 1 2 2 2 0 0 1

2 15 2 0 0 0 0 1 0

0 1 4 0 0 0 0 0 4 0 0 23 0 1 0 1 2 1 1 0 1

1 18 2 1 0 0 0 0 0

0 3 3 0 1 0 0 0 3 1 0 28 0 1 0 1 1 1 0 0 1

3 36 4 1 0 0 0 0 0

5

0 2 4 0 0 0 0 0 2 1 0 25 1 0 1 2 2 1 1 0 0

115 0 12 2 0 0 0 1 0 0

0

2 4 3 0 0 1 0 0 1 1 0 35 0 0 1 1 2 1 0 0 1

116 3 48 4 0 0 0 1 0 0

8

4 4 4 0 1 0 0 0 1 1 0 47 0 0 1 1 2 1 1 0 1

0 42 2 0 0 0 1 0 0

4 3 4 0 0 0 0 0 3 0 0 30 0 0 1 1 3 1 1 0 0

0 10 4 0 0 1 0 0 0

4 1 2 0 0 0 1 0 3 1 0 27 0 1 0 2 2 1 0 1 1

119 0 33 4 0 0 1 0 0 0

2 2 1 0 0 0 0 0 4 0 0 23 0 0 1 2 2 1 0 0 0

1 12 4 1 0 0 0 0 0

6

2 3 3 1 0 0 0 0 3 0 0 36 0 0 1 1 3 1 1 0 1

121 0 21 2 0 0 0 1 0 0

5

0 2 3 0 0 0 0 0 2 1 0 25 0 0 1 2 2 1 1 0 0

122 3 24 4 0 1 0 0 0 0

8

0 4 4 0 0 0 0 0 2 0 0 41 0 1 0 2 3 1 1 0 1

3 12 2 0 0 1 0 0 0

8

0 2 3 0 1 0 0 0 2 1 0 24 0 1 0 1 1 1 0 0 1

2 10 4 1 0 0 0 0 0

0 4 4 0 1 0 0 0 4 0 1 63 0 0 0 2 2 1 1 0 1

1 18 2 0 0 1 0 0 0

4

4 1 4 0 0 0 0 0 3 1 0 27 0 1 0 1 2 1 0 0 0

126 0 12 4 1 0 0 0 0 0

1

0 2 4 0 1 0 0 0 2 0 0 30 0 0 1 2 2 1 0 0 1

0 12 2 0 0 0 1 0 0

0 2 4 0 0 1 0 0 2 1 0 40 0 0 1 1 1 1 0 0 1

128 1 12 2 0 0 0 0 0 0

0 2 4 0 1 0 0 0 2 0 0 30 0 0 1 1 2 1 0 0 0

129 1 12 4 0 1 0 0 0 0

0 0 4 0 1 0 0 0 2 0 0 34 0 0 1 2 3 1 1 0 1

0 12 4 1 0 0 0 0 0

9

0 2 3 0 0 0 1 0 2 1 0 29 0 0 1 2 2 1 0 0 0

131 1 48 2 1 0 0 0 0 0

4 3 1 0 0 0 0 0 2 0 0 24 0 0 1 1 2 1 0 0 1

132 0 36 3 0 0 0 0 1 0

7

0 2 4 0 1 0 0 0 3 0 0 29 1 0 1 1 2 1 1 0 0

133 3 15 2 0 0 1 0 0 0

8

0 1 2 0 1 0 0 0 3 0 0 27 1 0 1 2 1 1 0 0 1

134 3 18 2 0 0 1 0 0 0

4

0 2 4 0 1 0 0 0 4 0 1 47 1 0 0 2 2 1 0 0 1

135 3 60 2 0 0 0 1 0 0

1 3 2 0 0 0 0 0 4 1 0 21 0 0 1 1 2 1 1 0 1

3 12 4 0 0 0 1 0 0

4 4 4 0 0 0 0 0 2 1 0 38 0 0 1 2 2 1 1 0 1

3 27 3 0 1 0 0 0 0

3 2 2 0 1 0 0 0 2 0 0 27 0 0 1 2 2 1 0 0 1

1 12 2 0 0 0 1 0 0

2 2 4 0 1 0 0 0 3 1 0 66 0 0 1 1 1 1 0 0 0

139 1 15 4 0 0 0 1 0 0

8

4 3 4 0 1 0 0 1 2 1 0 35 1 0 1 3 2 1 1 0 1

140 2 12 2 0 0 0 1 0 0

1

0 2 2 0 0 0 0 0 2 0 0 44 0 1 0 1 1 1 1 0 1

141 2 6 2 1 0 0 0 0 0

3 1 2 0 0 1 0 0 2 1 0 27 0 0 1 1 0 1 0 1 1

1 36 2 0 0 0 1 0 0

5

0 1 4 0 0 0 0 0 1 0 1 30 0 0 1 1 3 1 1 0 1

143 0 27 2 0 0 0 1 0 0

6

0 2 3 0 1 0 0 0 2 0 0 27 0 0 1 1 3 1 0 0 1

144 0 18 2 0 0 1 0 0 0

2

0 2 2 0 1 0 0 0 2 0 0 22 0 0 1 1 2 1 0 0 0

3 21 4 0 0 1 0 0 0

0 1 4 0 0 0 0 0 4 0 0 23 0 0 1 1 2 1 1 0 1

146 1 48 1 0 0 0 0 0 1

6

1 3 4 0 1 0 0 0 2 0 0 30 0 0 1 1 2 1 0 0 1

0 6 4 1 0 0 0 0 0 860 0 4 1 0 0 0 0 0 4 0 1 39 0 0 1 2 2 1 1 0 1

3 12 4 1 0 0 0 0 0

1 3 4 0 0 0 0 0 3 0 0 51 0 0 1 2 2 1 1 0 1

0 36 4 0 0 1 0 0 0

0 2 3 0 1 0 0 1 2 0 0 28 0 0 1 2 2 1 0 0 1

3 18 4 0 0 0 1 0 0

2

3 4 4 0 1 0 0 0 4 0 0 46 0 0 1 2 2 1 0 0 1

3 6 2 0 0 0 1 0 0

1 4 2 0 1 0 0 0 4 0 1 42 1 0 0 1 2 2 1 0 1

152 3 10 2 0 0 0 1 0 0 1

0 2 1 0 1 0 0 0 4 0 0 38 0 0 1 1 2 1 1 1 1

153 2 36 2 0 0 0 1 0 0

8

0 2 4 0 1 0 0 0 1 0 0 24 0 0 1 1 2 1 0 0 1

1 24 4 0 1 0 0 0 0

3 4 2 0 0 0 0 0 4 0 1 29 0 1 0 1 2 1 0 0 1

1 24 3 0 0 0 0 0 1

7

1 3 4 0 1 0 0 0 4 0 0 36 0 1 0 1 3 1 1 0 1

156 0 12 2 0 0 1 0 0 0

0 2 2 0 0 0 0 0 4 0 0 20 0 1 0 1 2 1 0 0 0

0 9 4 0 0 0 0 0 0

1 4 3 0 1 0 0 1 4 1 0 48 0 0 1 2 2 2 0 1 1

158 0 12 1 0 0 0 0 0 0

0 4 4 0 0 1 0 0 1 0 0 45 1 0 1 1 1 1 0 0 1

159 1 24 2 1 0 0 0 0 0

2

1 3 2 0 1 0 0 0 3 0 0 38 1 0 1 2 2 1 1 0 1

3 6 4 0 0 0 1 0 0

8

4 2 1 0 1 0 0 0 2 1 0 34 0 0 1 2 1 2 0 0 1

3 24 4 0 0 0 1 0 0

2

1 4 3 0 1 0 0 0 4 1 0 36 0 0 1 1 2 2 1 0 1

3 18 4 1 0 0 0 0 0

0 1 4 0 0 0 0 0 1 0 0 30 0 0 1 2 2 1 0 0 1

3 15 2 0 0 0 0 0 0

2 3 4 0 1 0 0 0 3 0 0 36 0 0 1 2 2 1 1 0 1

1 10 2 1 0 0 0 0 0

0 0 2 0 1 0 0 0 4 0 1 70 1 0 0 1 3 1 1 0 1

3 36 2 1 0 0 0 0 0

2 4 4 0 1 0 0 0 4 0 0 36 0 0 1 1 2 1 0 0 1

3 6 2 0 0 1 0 0 0

8

2 2 1 0 1 0 0 0 2 0 0 32 0 0 1 1 2 1 1 0 1

0 18 2 0 0 1 0 0 0

0 0 4 0 0 0 0 0 2 0 0 33 0 0 1 1 2 1 0 0 0

168 1 11 2 0 0 1 0 0 0

3 1 4 0 0 0 0 0 1 1 0 20 0 0 1 1 2 1 0 0 1

3 24 2 0 0 1 0 0 0

2

0 3 2 0 0 0 0 0 4 0 0 25 0 1 0 1 2 1 1 0 1

1 24 4 0 0 0 0 0 1

5

0 4 4 1 0 0 0 0 4 1 0 31 0 0 1 2 2 1 1 0 0

0 15 0 1 0 0 0 0 0

0 4 4 0 1 0 0 0 3 0 0 33 0 1 0 2 2 2 0 0 0

3 12 2 0 0 1 0 0 0

0 2 4 0 0 0 0 0 1 1 0 26 0 0 1 1 2 1 1 0 1

1 24 3 0 0 1 0 0 0

0 0 3 0 0 0 0 0 2 0 0 34 0 0 1 1 3 1 1 0 0

174 1 8 2 0 0 0 1 0 0

0 2 4 0 1 0 0 1 2 1 0 33 0 0 1 1 2 1 0 1 1

175 0 21 3 0 0 0 0 1 0

0 1 2 0 1 0 0 0 1 0 0 26 0 0 1 2 2 1 0 0 0

176 3 30 1 0 1 0 0 0 0

4 0 4 0 0 0 0 0 1 1 0 53 1 0 1 1 3 1 1 0 0

0 12 2 0 0 1 0 0 0

7

0 2 2 1 0 0 0 0 1 0 0 42 0 0 1 1 2 1 0 0 1

0 6 4 0 0 0 1 0 0

2 4 4 0 1 0 0 0 4 0 0 52 0 0 1 2 2 1 0 0 1

3 12 2 0 0 0 1 0 0

0 3 4 0 1 0 0 0 2 0 0 31 0 1 0 2 3 2 1 0 1

180 0 21 4 1 0 0 0 0 0

0 4 4 0 1 0 0 0 4 1 0 65 0 0 1 2 2 1 0 0 1

181 3 36 3 0 0 0 0 0 1

0 1 1 1 0 0 0 0 1 0 0 28 0 0 1 2 2 1 0 0 0

1 36 3 0 0 0 0 0 1

5

0 2 2 1 0 0 0 0 2 1 0 30 1 0 1 2 3 1 1 0 0

183 0 21 1 1 0 0 0 0 0

4 2 4 0 1 0 0 0 2 0 0 40 0 0 1 2 1 2 0 0 0

3 24 4 0 0 1 0 0 0

7

3 2 4 0 1 0 0 0 4 1 0 50 0 0 1 1 2 1 1 0 1

1 18 4 1 0 0 0 0 0

0 4 4 0 1 0 0 0 4 0 0 36 1 0 1 1 2 2 1 0 0

3 15 4 0 0 0 1 0 0

0 2 4 0 1 0 0 0 2 0 0 31 0 0 1 2 2 1 0 0 1

1 9 1 0 1 0 0 0 0

9

0 4 2 0 0 0 0 0 4 0 1 74 1 0 0 1 3 2 1 0 0

188 1 16 4 1 0 0 0 0 0

0 0 2 0 1 0 0 0 3 0 0 68 0 0 0 3 0 1 1 0 1

189 0 12 2 0 0 0 1 0 0

1 3 4 0 0 1 0 0 1 0 0 20 0 0 1 1 2 1 0 0 0

190 1 18 0 0 0 1 0 0 0

4

0 2 1 0 0 0 0 0 4 0 0 33 1 0 1 2 2 1 1 0 1

191 3 24 2 0 0 0 0 0 1

1

3 2 2 0 1 0 0 0 3 0 0 54 0 0 1 3 3 1 1 0 0

192 1 48 0 0 0 0 0 0 1

4

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4 3 3 0 1 0 0 0 2 0 0 31 1 0 1 1 3 2 1 0 1

897 0 21 2 0 0 0 1 0 0

0 1 4 0 0 0 0 0 4 0 0 28 0 1 0 1 3 1 1 0 1

3 12 4 0 0 1 0 0 0

3 3 3 0 0 0 0 0 2 0 0 35 0 0 1 1 2 1 0 1 1

899 3 15 2 0 0 1 0 0 0

4 3 1 0 0 0 0 0 4 1 0 33 1 1 0 1 1 1 0 0 1

900 0 18 2 0 0 1 0 0 0

0 2 2 0 1 0 1 0 3 0 0 42 0 0 1 1 2 1 0 0 0

901 0 16 4 1 0 0 0 0 0

0 4 2 0 1 0 0 1 4 0 0 43 1 1 0 1 2 1 1 0 0

902 3 20 4 1 0 0 0 0 0

4 1 2 1 0 0 0 0 4 1 0 44 0 0 1 2 2 1 1 0 1

903 3 36 4 0 1 0 0 0 0

4 4 2 0 1 0 0 0 4 0 1 42 0 0 0 2 2 1 0 0 1

3 15 2 0 0 0 1 0 0 1386 4 2 4 0 0 1 0 0 2 1 0 40 0 1 0 1 2 1 1 0 1

905 3 24 2 0 0 0 1 0 0

0 4 4 0 1 0 0 0 1 1 0 36 0 0 1 1 3 1 1 0 1

0 12 2 0 0 0 1 0 0

0 2 2 0 1 0 0 0 2 1 0 20 0 1 0 1 3 2 1 0 1

907 0 21 2 1 0 0 0 0 0

4 3 2 0 1 0 1 0 2 1 0 24 0 0 1 1 1 1 0 1 1

908 1 36 2 0 0 0 0 1 0

4 2 2 0 0 1 0 0 2 0 0 27 0 0 1 1 2 1 0 0 1

909 3 15 3 0 1 0 0 0 0

0 1 1 0 0 0 0 0 2 0 0 46 0 0 1 2 1 1 0 0 1

910 1 9 2 1 0 0 0 0 0

4 2 1 0 0 0 0 0 2 1 0 33 0 0 1 1 1 1 0 0 1

3 36 3 0 0 0 1 0 0

0 2 4 0 0 0 0 0 4 1 0 34 0 0 1 2 2 1 0 0 1

1 24 4 0 0 1 0 0 0

0 1 2 0 0 0 0 0 4 0 0 25 1 0 1 1 1 1 0 0 0

1 30 2 0 0 0 1 0 0

4 4 2 0 0 0 0 0 4 0 0 25 0 0 1 1 2 1 0 0 1

914 3 11 2 0 0 0 0 0 1

3 4 1 1 0 0 0 0 2 1 0 28 0 0 1 1 2 1 1 0 1

915 0 24 1 0 0 0 0 0 1

0 2 4 0 1 0 0 0 2 0 0 31 0 1 0 1 2 1 1 0 0

1 48 0 0 0 0 0 0 0

0 2 1 0 0 0 0 0 2 0 0 32 1 0 1 1 3 1 1 1 0

3 10 2 0 1 0 0 0 0

1 2 1 0 1 0 1 0 2 1 0 32 0 0 1 1 2 2 0 0 1

918 0 6 2 1 0 0 0 0 0

0 4 1 0 1 0 0 0 4 0 1 68 1 0 1 1 3 1 1 0 0

0 24 2 0 0 1 0 0 0

1 0 1 1 0 0 0 0 1 0 0 33 0 0 1 1 2 1 0 0 0

0 24 2 0 0 1 0 0 0

0 4 4 0 1 0 0 0 2 0 0 39 0 1 0 1 3 1 1 0 0

3 18 4 0 0 1 0 0 0

0 2 4 0 0 0 0 0 2 0 1 28 0 0 1 2 2 1 0 0 1

922 3 48 3 0 0 0 1 0 0

2 3 4 0 1 0 0 0 1 0 0 37 0 0 1 1 3 1 1 0 1

0 9 2 0 0 0 1 0 0

0 1 3 0 0 0 0 0 4 0 0 22 0 1 0 1 2 1 0 0 0

924 1 12 2 1 0 0 0 0 0

0 3 3 0 1 0 0 0 4 0 0 30 0 1 0 1 2 2 1 0 1

925 0 24 1 0 0 1 0 0 0

0 1 2 1 0 0 0 0 1 0 0 55 1 0 1 1 2 1 1 0 0

926 0 12 1 1 0 0 0 0 0 697 0 1 4 0 1 0 0 0 2 0 0 46 1 0 1 2 2 1 1 0 0
927 0 18 4 0 0 1 0 0 0

0 1 4 0 0 0 0 0 4 0 0 21 0 1 0 1 2 1 0 0 1

928 0 48 2 0 1 0 0 0 0

0 3 4 0 1 0 0 0 4 0 1 39 1 0 0 3 2 2 1 0 0

3 30 2 0 0 0 1 0 0

4 4 4 0 1 0 0 0 4 0 0 58 0 0 1 1 2 1 1 0 1

930 0 12 3 1 0 0 0 0 0 1344 0 2 4 0 1 0 0 0 2 1 0 43 0 0 1 2 1 2 0 0 1
931 0 24 2 0 0 1 0 0 0

0 1 4 0 1 0 1 0 1 0 0 24 0 0 1 1 1 1 0 1 1

932 1 9 2 0 0 0 1 0 0

0 1 4 0 0 0 0 0 2 0 0 22 0 0 1 1 2 1 1 0 0

933 3 9 4 1 0 0 0 0 0

0 2 3 0 1 0 0 0 1 1 0 30 0 0 1 2 2 1 0 0 1

934 3 12 4 0 0 0 1 0 0 522 2 4 4 0 1 0 0 0 4 0 0 42 0 0 1 2 2 2 1 0 1
935 0 12 2 0 0 0 1 0 0

0 2 4 0 0 0 0 0 1 0 0 23 1 0 1 1 2 1 0 0 1

936 1 30 3 0 0 0 1 0 0

1 1 4 0 1 0 0 0 3 0 1 30 1 0 1 2 3 1 0 0 0

937 2 9 2 0 0 0 1 0 0 745 0 2 3 0 0 0 0 0 2 1 0 28 0 0 1 1 1 1 0 0 0
938 1 6 2 0 0 0 1 0 0

0 1 4 0 0 1 0 0 3 0 0 30 0 1 0 1 3 1 1 0 1

939 1 60 2 0 0 0 0 1 0

0 2 4 0 1 0 0 0 4 0 1 42 0 0 0 1 2 1 0 0 0

940 3 24 4 0 1 0 0 0 0

4 2 2 0 1 0 0 0 4 0 0 46 0 0 1 2 3 2 1 0 1

941 3 12 2 1 0 0 0 0 0

4 1 2 0 1 0 0 0 3 0 0 45 0 0 1 1 3 2 1 0 1

3 10 2 1 0 0 0 0 0 1546 0 2 3 0 1 0 0 0 2 1 0 31 0 0 1 1 1 2 0 1 1

943 3 24 2 0 0 1 0 0 0 929 4 3 4 0 1 0 0 0 2 0 0 31 1 0 1 1 2 1 1 0 1

3 4 4 1 0 0 0 0 0

0 3 2 0 1 0 0 0 1 1 0 42 0 0 1 3 1 2 0 0 1

0 15 2 0 0 1 0 0 0

0 1 4 0 0 0 0 1 1 0 0 46 0 1 0 1 2 1 0 0 1

1 48 0 1 0 0 0 0 0

2 1 1 0 0 0 0 0 1 0 0 30 0 0 1 2 2 1 0 0 1

947 0 24 1 0 0 1 0 0 0 3349 2 1 4 0 1 0 0 0 4 0 1 30 0 0 0 1 2 2 1 0 0

3 12 2 1 0 0 0 0 0

4 0 4 0 1 0 0 0 4 0 1 38 0 0 1 1 3 1 1 0 1

3 18 2 0 0 1 0 0 0

0 1 4 0 0 1 1 0 1 0 0 43 0 0 1 1 1 2 0 0 0

950 3 24 2 0 0 0 1 0 0

1 4 2 0 1 0 0 0 4 0 0 31 0 0 1 2 2 1 0 0 0

951 1 18 4 0 0 0 0 0 1 3590 0 0 3 0 0 1 0 0 3 0 0 40 0 0 1 3 0 2 1 0 1

0 36 3 0 0 0 0 0 1

0 3 2 0 1 0 0 0 1 0 0 24 0 0 1 2 2 1 1 0 0

1 24 2 0 1 0 0 0 0

2 1 3 0 0 0 0 0 4 0 0 28 0 1 0 1 2 1 0 0 0

954 3 36 2 0 0 1 0 0 0

0 0 4 0 0 0 0 0 2 0 0 26 0 0 1 2 3 1 1 0 0

0 12 2 1 0 0 0 0 0

0 2 4 0 0 0 0 1 4 0 0 29 0 0 1 1 2 1 1 0 1

0 24 4 0 0 0 1 0 0 1231 3 4 4 0 0 0 0 0 4 0 0 57 0 1 0 2 3 1 1 0 1

957 2 30 4 0 0 0 1 0 0

4 4 4 0 1 0 0 0 4 0 0 49 1 0 1 2 1 1 0 0 1

958 1 9 4 0 0 0 1 0 0 1154 0 4 2 0 1 0 0 0 4 1 0 37 0 0 1 3 1 1 0 0 1
959 0 28 2 1 0 0 0 0 0

0 2 3 0 1 0 0 0 2 0 0 45 0 0 1 1 1 1 0 0 0

960 1 24 2 0 0 1 0 0 0

1 4 4 0 1 0 0 0 4 0 1 30 0 0 0 1 2 1 0 0 1

961 3 6 4 0 0 0 1 0 0

0 4 2 0 0 1 0 0 2 1 0 30 0 1 0 2 2 1 0 0 1

962 1 21 3 1 0 0 0 0 0

0 2 1 1 0 0 0 0 4 0 0 47 0 0 1 2 2 1 0 0 1

963 3 15 2 1 0 0 0 0 0

4 2 3 0 1 0 0 0 2 0 1 29 0 0 1 1 2 1 0 0 1

964 3 24 2 0 0 0 1 0 0

2 4 3 0 1 0 0 0 2 0 0 35 1 0 1 2 2 1 1 0 0

965 1 6 2 0 0 0 0 0 0 454 0 1 3 0 0 1 0 0 1 0 0 22 0 0 1 1 1 1 0 0 1
966 1 30 2 0 0 0 1 0 0

4 2 4 0 0 0 0 0 1 0 0 26 0 0 1 1 2 1 0 0 1

967 1 27 4 0 0 0 1 0 0

2 2 4 0 1 0 0 0 2 0 0 23 0 0 1 2 1 1 0 0 0

3 15 2 0 0 0 1 0 0

0 4 4 0 0 0 0 0 2 0 0 54 1 1 0 1 3 1 1 0 1

969 3 42 2 0 0 0 1 0 0

4 3 2 0 0 1 0 0 4 0 0 29 0 1 0 1 2 1 1 0 1

0 11 4 1 0 0 0 0 0

0 2 1 0 1 0 0 0 2 1 0 40 0 0 1 2 1 2 0 0 1

1 15 2 0 0 0 0 0 0

1 2 4 0 1 0 0 1 2 1 0 22 0 0 1 1 2 1 0 0 1

3 24 2 1 0 0 0 0 0

0 2 1 0 1 0 0 0 4 0 0 43 0 0 1 1 1 2 0 0 1

973 0 24 1 1 0 0 0 0 0

0 0 1 0 0 0 1 0 4 0 1 29 0 1 0 2 0 1 0 0 0

974 0 60 2 0 0 0 0 0 1

0 4 4 0 1 0 1 0 4 0 1 36 0 1 0 1 2 1 0 0 0

975 3 30 4 0 0 0 1 0 0

0 2 4 0 0 0 0 0 2 0 0 33 0 0 1 1 2 1 1 0 1

976 2 24 2 0 0 0 1 0 0 1258 2 2 3 0 0 0 0 0 3 0 0 57 0 0 1 1 1 1 0 0 1

1 6 2 0 0 0 1 0 0 753 0 2 2 0 0 0 0 1 3 1 0 64 0 0 1 1 2 1 0 0 1

978 1 18 3 0 0 0 0 0 1

4 4 4 0 1 0 0 0 2 0 0 42 0 0 1 2 2 1 0 0 1

979 3 24 3 1 0 0 0 0 0

0 4 4 0 1 0 0 0 4 0 0 47 0 0 1 2 1 2 0 0 0

980 1 15 1 1 0 0 0 0 0

1 2 2 0 0 1 0 0 2 0 0 25 0 1 0 1 2 1 0 0 0

1 30 4 0 0 1 0 0 0

0 3 2 0 1 0 0 0 2 0 0 49 0 0 1 1 2 1 0 0 0

3 48 2 0 0 0 0 0 1

0 0 3 0 1 0 0 0 2 0 0 33 1 1 0 1 3 1 1 0 0

983 2 21 2 1 0 0 0 0 0

1 2 1 0 0 0 0 0 1 0 0 28 1 0 1 1 3 1 1 0 1

0 36 2 0 1 0 0 0 0

0 2 2 0 1 0 0 0 2 0 0 26 0 0 1 1 2 2 0 0 0

3 24 4 0 0 1 0 0 0 2028 0 3 2 0 1 0 0 0 2 0 0 30 0 0 1 2 1 1 0 0 1

0 15 4 0 0 1 0 0 0

0 2 4 0 0 0 0 0 3 0 0 25 0 1 0 2 2 1 0 0 1

987 2 42 0 0 0 0 0 0 1

0 1 2 1 0 0 0 0 1 0 0 33 0 0 1 2 2 1 0 0 1

3 13 2 0 0 0 1 0 0

1 0 2 0 0 0 0 0 4 1 0 64 0 0 1 1 2 1 0 0 1

0 24 2 0 1 0 0 0 0

0 0 4 0 1 0 0 0 2 0 1 29 0 0 0 1 3 1 1 0 1

990 1 24 4 0 0 0 1 0 0 1743 0 4 4 0 1 0 0 0 2 0 0 48 0 0 1 2 1 1 0 0 1
991 3 12 4 0 0 0 0 1 0

4 1 2 0 1 0 0 0 1 0 0 37 0 0 1 2 1 2 0 0 1

3 15 1 0 0 0 1 0 0

1 4 4 0 1 0 0 0 4 0 0 34 1 0 1 1 1 2 0 0 1

993 0 18 2 0 0 0 1 0 0

4 3 2 0 0 1 0 0 4 0 0 23 0 1 0 2 1 1 0 0 1

0 36 2 0 0 1 0 0 0 3959 0 0 4 0 1 0 0 0 3 0 0 30 0 0 1 1 3 1 1 0 1

995 3 12 2 1 0 0 0 0 0

4 4 4 0 1 0 0 0 3 0 0 50 0 0 1 1 2 1 1 0 1

996 3 12 2 0 0 1 0 0 0

0 3 3 0 0 0 0 0 4 1 0 31 0 0 1 1 1 1 0 0 1

0 30 2 0 1 0 0 0 0

0 2 4 1 0 0 0 0 4 0 0 40 0 0 1 1 3 1 1 0 1

998 3 12 2 0 0 0 1 0 0 804 0 4 4 0 1 0 0 0 4 0 0 38 0 0 1 1 2 1 0 0 1
999 0 45 2 0 0 0 1 0 0 1845 0 2 4 0 1 0 0 0 4 0 1 23 0 0 0 1 2 1 1 0 0
1000 1 45 4 0 1 0 0 0 0

1 0 3 0 1 0 0 0 4 0 0 27 0 0 1 1 2 1 0 0 1

MALE_MAR_or_WID
11

69 67
48 59 51
20

96 49
42 78 82 45
48

 70 53
36 90 55 35
2

83
6

94
305 61
52 34
12

95
43
1

 56
11

99 60
1

 40
128
242
80 72
12

 57 44
343
2

134
26

 47
22

 41
180
206
1

 37
426 39
409
241
68 63
1

91
402
33 58 66
12

 64
14

 74
47

 46
6

110
38 210
122
458
233
115
62
61

87
6

143
13

93
229
135
7

228
20

73
333
59

 65
126
54 337
222
783
6

468
9

566
196
622
139
153
195
144
31

81
5

190
21

71
1007
181
239
8

133
730
116
595
75 19

77
76 152
396
477
79 9

436
383
594
121
156
84 175
85 231
86 141
129
88 1

261
89 224
1

108
618
92 140
7

97
361
131
159
201
98 262
2337
705
101 146
102 232
103 932
104 191
105 244
106 119
107 645
607
109 7

721
1

410
111 1

449
112 392
113 6

260
114 785
168
357
117 7

174
118 2

132
4

281
120 236
183
386
123 176
124 781
125 192
212
127 701
639
1

 860
130 349
8

487
688
270
198
10144
136 1

240
137 8

613
138 766
272
188
709
142 479
341
246
145 2

288
356
147
148 682
149 5

371
150 158
151 1

346
924
584
154 7

758
155 696
1

282
157 1288
339
351
160 189
161 287
162 1055
163 1262
164 7

308
165 909
166 297
167 1131
1

577
169 397
170 193
171 950
172 763
173 2064
1

414
3414
7

485
177 257
178 338
179 1

963
571
9

572
182 445
1

647
184 377
185 884
186 1

360
187 512
1175
674
324
459
384
3

 915
194 2108
303
1

501
197 1

382
951
199 276
429
936
202 1168
203 511
204 902
205 1

495
10

623
207 1

935
208 1

424
209 6

568
1

413
211 307
3

835
213 5

293
214 1

908
215 334
216
217 310
218 391
219 302
220 1

364
221 625
1200
223 707
978
225 465
226 2613
227 10

961
786
1

478
230 314
421
250
2141
234 866
235 1

544
1

823
237 1

455
238 2

767
1

291
252
1595
243 460
1185
245 344
1

258
247 717
248 1204
249 1

925
433
251 666
2251
253 2150
254 415
255 2030
256 7

418
268
2149
259 381
1154
1

657
1

603
263 530
264 274
265 1231
266 802
267 6

304
1

533
269 897
999
271 2

 662
1402
273 1216
306
275 1199
2

 697
277 2

404
278
279 461
280 1

901
336
1

574
283 1445
284 1

520
285 387
286 10

722
4

788
7

582
289 1092
290 1024
1076
292 9

398
6

419
294 4

796
295 7

 629
296 9

 960
467
298 1287
299 2

515
300 2

 745
301 672
380
1344
1038
10127
1

543
481
727
309 1237
311 538
312 551
313 374
685
315 1

494
316 2

746
317 708
318 435
319
320 3

643
321 4249
322 1

938
323 2

910
2

659
325 1028
326 3398
327 580
328 1

525
329 447
330 1068
331 6

615
332 1

864
7

408
11

 590
335 411
3384
2101
1275
416
340 1

521
5

743
342 359
3213
4

439
345 394
1459
347 882
348 375
1743
350 1136
1236
352 959
353 3229
354 619
355
1246
2331
358 446
776
2

406
1239
362 3

399
363 2247
1766
365 2

473
366 1

542
367 385
368 3

650
369 3446
370 3001
3079
372 6070
373 2146
13

756
1478
376 768
2320
378 846
379 1

431
2212
1297
1283
1330
427
2238
1126
388 737
389 2326
390
1

820
983
393 3249
1

957
395
11

 760
2

578
2348
1223
400 1

516
401 1473
1

887
403 8

648
405 2

899
2039
407 2197
1053
3235
939
1

967
412 725
2292
1

597
1381
5

842
417 2

579
8

471
2

782
420 1042
3186
422 2028
423 958
1

591
425 2

762
2

779
2743
428 1149
1313
430 1190
3

 448
432 11328
1

872
434 2058
2136
1

 484
437 660
438
3394
440 609
441 1884
442 1

620
443 2629
444 719
509
1244
1842
2

576
1424
450 1512
451 11054
452 518
453 2

 759
454 2

670
4

817
456 2

679
457 3

905
3386
4594
3620
462 1721
463 3017
464 754
1950
466 2924
1659
723
469 2

764
470 4679
3092
472
654
474 1238
475 1245
476 3114
2

569
5152
1037
480
3

573
482 1201
483 3622
1163
486 1209
3077
488 3

757
489 1418
490 3518
491 1

934
492 831
493
2122
496 2

 996
497 903
498 1

 585
499 1301
1323
3123
502 549
503
504
505 1207
506 1309
507 2360
508 6

850
1413
510 8

588
4

686
513 2

687
514
2255
517 1361
712
519 1203
700
550
522 3190
523 711
524 3488
1113
526 7

966
527 1

532
528 1503
529 2302
531 2273
2

631
534 1311
535 3105
536 2319
537 1374
3

612
539 7763
540 3049
541 1534
2032
635
2864
545 1255
546 1333
547 2022
548 1

552
626
8

858
1

 750
553 699
554 1

995
555
556 1331
557 2278
558 5003
559 3552
560 1

928
561 2

964
562 1546
563 683
564 1

2389
565 4712
1553
567 1372
3

979
570 675
3234
954
5433
806
575 1082
2788
2

 930
1

927
2820
937
581 1056
3124
583 1388
2384
2133
586
587 2

799
1289
589 1217
2246
592 1

965
593 1572
2

718
1358
596 931
1442
598 4241
599 2

775
600 3

863
601 2329
602 918
1

837
604 3349
605
606 2

828
4526
608 2

671
2051
610 1300
611 741
1240
3357
614 3

632
1

808
616 12204
617 9157
3

676
3441
640
621 3

 652
1530
3

914
624 1858
2600
1979
627 2116
628 1437
4042
630 3

832
3660
633 1444
634 1

980
1355
636 1393
637 1376
638 15

653
1493
4370
641
642 1308
4623
644 1

851
1

880
646 798
4583
1386
649 947
684
651 747
1

922
2303
8086
655 2346
656 3

973
888
658 10222
4221
6361
661
900
663 2241
664 1050
665 1047
6314
667 3496
668 3609
669 4

843
4139
5

742
673 10366
2080
2580
4530
677 5150
678 5595
680 1453
681 1538
2279
5103
9

857
6527
1347
2

862
689 2

 753
690 3651
691 975
692 2631
693 2

896
694 4

716
695 2284
1103
698 926
1

800
1905
1123
702 6331
703 1377
704 2503
2528
706 5324
6560
2

969
1206
710 2118
1198
713 2476
714 1138
715 14027
7596
1505
3148
720 6148
1337
1228
724 790
2570
726
1316
728 1882
729 6416
731 6403
732 1

987
733
734 2603
735 3380
736 3

990
11560
738 4380
739 6

761
740 4280
2325
1048
3160
744 2483
14179
1

797
2511
748 1274
749 5248
3029
751
752 976
841
5

771
755 1555
1285
1299
1271
5045
2124
2214
12680
765 2463
1155
3108
2901
769 3617
770 1655
2

812
772 8065
773 3275
774 2223
1480
1371
777 3535
778 3509
5711
780 3872
4

933
1

940
1410
784 836
6468
1

941
787 2675
2751
789 6224
5

998
791 1188
792 6313
793 1221
794 2

892
795 3062
2301
7511
1258
1549
801 1597
1795
803 4272
804
805 7472
9271
807
809 9283
810 1778
811 907
813 962
814 3051
815 3931
816 7432
1338
818 1554
819 15857
1345
821 1101
822 3016
2712
824
825 3780
826 1602
827 3966
4165
829 833
830 6681
2375
11816
834 5084
2327
886
838
839 2957
840 2611
5179
2

993
1

943
844 1559
845 3422
3976
847 6761
848 1249
849 1364
2235
852
853 1471
854
855 10

875
856 1474
894
3343
859 3959
3577
861 5804
2169
2439
865 2210
2221
867
868 3331
869 7409
870
871 7678
1343
873 1382
874
3590
876 1322
877
878 3595
879 1422
6742
881 7814
9277
883 2181
1098
885 4057
2825
15672
889 6614
890 7824
891 2442
1829
893 2171
5800
895 1169
8947
2606
898 1592
2186
4153
2625
3485
10477
904
1278
906 1107
3763
3711
3594
3195
911 4454
912 4736
913 2

991
2142
3161
916 18424
917 2848
14896
919 2359
920 3345
921 1817
12749
923 1366
2002
6872
1049
10297
929 1867
1747
1670
1224
1498
1919
2063
6288
6842
3527
942
944 1455
945 1845
946 8358
948 2859
949 1533
3621
952 2145
953 4113
10

974
955 1893
956
3656
4006
3069
1740
2353
3556
2397
1715
2520
968 3568
7166
970 3939
971 1514
972 7393
1193
7297
2831
977
2427
2538
1264
981 8386
982 4844
2923
984 8229
985
986 1433
6289
988 1409
989 6579
3565
992 1569
1936
994
2390
1736
997 3857
4576

GERMAN CREDIT

GermanCredit.xls is the dataset for this case study.

Background

Money-lending has been around since the advent of money; it is perhaps the world’s second-oldest profession. The systematic evaluation of credit risk, though, is a relatively recent arrival, and lending was largely based on reputation and very incomplete data. Thomas Jefferson, the third President of the United States, was in debt throughout his life and unreliable in his debt payments, yet people continued to lend him money. It wasn’t until the beginning of the 20th century that the Retail Credit Company was founded to share information about credit. That company is now Equifax, one of the big three credit-scoring agencies (the other two are Transunion and Experian).

Individual and local human judgment are now largely irrelevant to the credit-reporting process. Credit agencies and other big financial institutions extending credit at the retail level collect huge amounts of data to predict whether defaults or other adverse events will occur, based on numerous customer and transaction information.

Data

This case deals with an early stage of the historical transition to predictive modeling, in which humans were employed to label records as either good or poor credit. The German Credit dataset has 30 variables and 1000 records, each record being a prior applicant for credit. Each applicant was rated as “good credit” (700 cases) or “bad credit” (300 cases). Figure 21.1 shows the values of these variables for the first four records. All the variables are explained in 

Table

21.6. New applicants for credit can also be evaluated on these 30 predictor variables and classified as a good or a bad credit risk based on the predictor variables.

The consequences of misclassification have been assessed as follows: The costs of a false positive (incorrectly saying that an applicant is a good credit risk) outweigh the benefits of a true positive (correctly saying that an applicant is a good credit risk) by a factor of 5. This is summarized in Table 21.4. The opportunity cost table was derived from the average net profit per loan as shown in Table 21.5.

Because decision makers are used to thinking of their decision in terms of net profits, we use these tables in assessing the performance of the various models.

Assignment

1. Review the predictor variables and guess what their role in a credit decision might be. Are there any surprises in the data?

2.

Divide the data into training and validation partitions, and develop classification models using the following data mining techniques in XLMiner: logistic regression and classification trees. Use variants of these techniques as you see fit (e.g. best subset, best pruned tree, etc.). (Note: Depending on the capacity of your computer, Best Subsets may not run quickly, and possibly not at all; if this is the case, choose another selection technique.)

3. Choose one model from each technique and report the confusion matrix and the cost/gain matrix for the validation data. Which technique has the most net profit?

4. Let us try and improve our performance. Rather than accept XLMiner’s initial classification of all applicants’ credit status, use the “predicted probability of success” in logistic regression (where success means 1) as a basis for selecting the best credit risks first, followed by poorer risk applicants.

· Sort the validation on “predicted probability of success.”

· For each case, calculate the net profit of extending credit.

· Add another column for cumulative net profit.

a. How far into the validation data do you go to get maximum net profit? (Often, this is specified as a percentile or rounded to deciles.)

b. If this logistic regression model is scored to future applicants, what “probability of success” cutoff should be used in extending credit?

Grading Comments

Question 1 not answered
For part 2, basic models were run but no variants were run to see if a more parsimonious model could be identified
Parts 3 and 4 not attempted or completed
As with the Week 6 Case, parts 3 and 4 are at the heart of data mining in practice. Merely running models with no critical evaluation of the results or
attempt to communicate what the models find will not suffice in the decision making world of business

Codelist

Codelist
Var. # Variable Name Description Variable Type Code Description
1.   OBS# Observation No. Categorical
2.   CHK_ACCT Checking account status Categorical 0 : < 0 DM 1: 0 < ...< 200 DM 2 : => 200 DM
3: no checking account
3.   DURATION Duration of credit in months Numerical
4.   HISTORY Credit history Categorical 0: no credits taken
1: all credits at this bank paid back duly
2: existing credits paid back duly till now
3: delay in paying off in the past
4: critical account
5.   NEW_CAR Purpose of credit Binary car (new) 0: No, 1: Yes
6. USED_CAR Purpose of credit Binary car (used) 0: No, 1: Yes
7. FURNITURE Purpose of credit Binary furniture/equipment 0: No, 1: Yes
8. RADIO/TV Purpose of credit Binary radio/television 0: No, 1: Yes
9. EDUCATION Purpose of credit Binary education 0: No, 1: Yes
10. RETRAINING Purpose of credit Binary retraining 0: No, 1: Yes
11. AMOUNT Credit amount Numerical
12. SAV_ACCT Average balance in savings account Categorical 0 : < 100 DM 1 : 100<= ... < 500 DM 2 : 500<= ... < 1000 DM 3 : =>1000 DM
4 : unknown/ no savings account
13. EMPLOYMENT Present employment since Categorical 0 : unemployed
1: < 1 year 2 : 1 <= ... < 4 years 3 : 4 <=... < 7 years 4 : >= 7 years
14. INSTALL_RATE Installment rate as % of disposable income Numerical
15. MALE_DIV Applicant is male and divorced Binary 0: No, 1: Yes
16. MALE_SINGLE Applicant is male and single Binary 0: No, 1: Yes
17. MALE_MAR_WID Applicant is male and married or a widower Binary 0: No, 1: Yes
18. CO-APPLICANT Application has a co-applicant Binary 0: No, 1: Yes
19. GUARANTOR Applicant has a guarantor Binary 0: No, 1: Yes
20. PRESENT_RESIDENT Present resident since – years Categorical 0: <= 1 year 1<…<=2 years 2<…<=3 years 3:>4years
21. REAL_ESTATE Applicant owns real estate Binary 0: No, 1: Yes
22. PROP_UNKN_NONE Applicant owns no property (or unknown) Binary 0: No, 1: Yes
23. AGE Age in years Numerical
24. OTHER_INSTALL Applicant has other installment plan credit Binary 0: No, 1: Yes
25. RENT Applicant rents Binary 0: No, 1: Yes
26. OWN_RES Applicant owns residence Binary 0: No, 1: Yes
27. NUM_CREDITS Number of existing credits at this bank Numerical
28. JOB Nature of job Categorical 0 : unemployed/ unskilled – non-resident
1 : unskilled – resident
2 : skilled employee / official
3 : management/ self-employed/highly qualified employee/ officer
29. NUM_DEPENDENTS Number of people for whom liable to provide maintenance Numerical
30. TELEPHONE Applicant has phone in his or her name Binary 0: No, 1: Yes
31. FOREIGN Foreign worker Binary 0: No, 1: Yes
32 RESPONSE Credit rating is good Binary 0: No, 1: Yes

Data

OBS# CHK_ACCT DURATION HISTORY NEW_CAR USED_CAR FURNITURE RADIO/TV EDUCATION RETRAINING AMOUNT SAV_ACCT EMPLOYMENT INSTALL_RATE MALE_DIV MALE_SINGLE MALE_MAR_or_WID CO-APPLICANT GUARANTOR PRESENT_RESIDENT REAL_ESTATE PROP_UNKN_NONE AGE OTHER_INSTALL RENT OWN_RES NUM_CREDITS JOB NUM_DEPENDENTS TELEPHONE FOREIGN RESPONSE
1 0 6 4 0 0 0 1 0 0 1169 4 4 4 0 1 0 0 0 4 1 0 67 0 0 1 2 2 1 1 0 1
2 1 48 2 0 0 0 1 0 0 5951 0 2 2 0 0 0 0 0 2 1 0 22 0 0 1 1 2 1 0 0 0
3 3 12 4 0 0 0 0 1 0 2096 0 3 2 0 1 0 0 0 3 1 0 49 0 0 1 1 1 2 0 0 1
4 0 42 2 0 0 1 0 0 0 7882 0 3 2 0 1 0 0 1 4 0 0 45 0 0 0 1 2 2 0 0 1
5 0 24 3 1 0 0 0 0 0 4870 0 2 3 0 1 0 0 0 4 0 1 53 0 0 0 2 2 2 0 0 0
6 3 36 2 0 0 0 0 1 0 9055 4 2 2 0 1 0 0 0 4 0 1 35 0 0 0 1 1 2 1 0 1
7 3 24 2 0 0 1 0 0 0 2835 2 4 3 0 1 0 0 0 4 0 0 53 0 0 1 1 2 1 0 0 1
8 1 36 2 0 1 0 0 0 0 6948 0 2 2 0 1 0 0 0 2 0 0 35 0 1 0 1 3 1 1 0 1
9 3 12 2 0 0 0 1 0 0 3059 3 3 2 1 0 0 0 0 4 1 0 61 0 0 1 1 1 1 0 0 1
10 1 30 4 1 0 0 0 0 0 5234 0 0 4 0 0 1 0 0 2 0 0 28 0 0 1 2 3 1 0 0 0
11 1 12 2 1 0 0 0 0 0 1295 0 1 3 0 0 0 0 0 1 0 0 25 0 1 0 1 2 1 0 0 0
12 0 48 2 0 0 0 0 0 1 4308 0 1 3 0 0 0 0 0 4 0 0 24 0 1 0 1 2 1 0 0 0
13 1 12 2 0 0 0 1 0 0 1567 0 2 1 0 0 0 0 0 1 0 0 22 0 0 1 1 2 1 1 0 1
14 0 24 4 1 0 0 0 0 0 1199 0 4 4 0 1 0 0 0 4 0 0 60 0 0 1 2 1 1 0 0 0
15 0 15 2 1 0 0 0 0 0 1403 0 2 2 0 0 0 0 0 4 0 0 28 0 1 0 1 2 1 0 0 1
16 0 24 2 0 0 0 1 0 0 1282 1 2 4 0 0 0 0 0 2 0 0 32 0 0 1 1 1 1 0 0 0
17 3 24 4 0 0 0 1 0 0 2424 4 4 4 0 1 0 0 0 4 0 0 53 0 0 1 2 2 1 0 0 1
18 0 30 0 0 0 0 0 0 1 8072 4 1 2 0 1 0 0 0 3 0 0 25 1 0 1 3 2 1 0 0 1
19 1 24 2 0 1 0 0 0 0 12579 0 4 4 0 0 0 0 0 2 0 1 44 0 0 0 1 3 1 1 0 0
20 3 24 2 0 0 0 1 0 0 3430 2 4 3 0 1 0 0 0 2 0 0 31 0 0 1 1 2 2 1 0 1
21 3 9 4 1 0 0 0 0 0 2134 0 2 4 0 1 0 0 0 4 0 0 48 0 0 1 3 2 1 1 0 1
22 0 6 2 0 0 0 1 0 0 2647 2 2 2 0 1 0 0 0 3 1 0 44 0 1 0 1 2 2 0 0 1
23 0 10 4 1 0 0 0 0 0 2241 0 1 1 0 1 0 0 0 3 1 0 48 0 1 0 2 1 2 0 1 1
24 1 12 4 0 1 0 0 0 0 1804 1 1 3 0 1 0 0 0 4 0 0 44 0 0 1 1 2 1 0 0 1
25 3 10 4 0 0 1 0 0 0 2069 4 2 2 0 0 1 0 0 1 0 0 26 0 0 1 2 2 1 0 1 1
26 0 6 2 0 0 1 0 0 0 1374 0 2 1 0 1 0 0 0 2 1 0 36 1 0 1 1 1 1 1 0 1
27 3 6 0 0 0 0 1 0 0 426 0 4 4 0 0 1 0 0 4 0 0 39 0 0 1 1 1 1 0 0 1
28 2 12 1 0 0 0 1 0 0 409 3 2 3 0 0 0 0 0 3 1 0 42 0 1 0 2 2 1 0 0 1
29 1 7 2 0 0 0 1 0 0 2415 0 2 3 0 1 0 0 1 2 1 0 34 0 0 1 1 2 1 0 0 1
30 0 60 3 0 0 0 0 0 1 6836 0 4 3 0 1 0 0 0 4 0 1 63 0 0 1 2 2 1 1 0 0
31 1 18 2 0 0 0 0 0 1 1913 3 1 3 0 0 1 0 0 3 1 0 36 1 0 1 1 2 1 1 0 1
32 0 24 2 0 0 1 0 0 0 4020 0 2 2 0 1 0 0 0 2 0 0 27 1 0 1 1 2 1 0 0 1
33 1 18 2 1 0 0 0 0 0 5866 1 2 2 0 1 0 0 0 2 0 0 30 0 0 1 2 2 1 1 0 1
34 3 12 4 0 0 0 0 0 1 1264 4 4 4 0 1 0 0 0 4 0 1 57 0 1 0 1 1 1 0 0 1
35 2 12 2 0 0 1 0 0 0 1474 0 1 4 0 0 0 0 0 1 0 0 33 1 0 1 1 3 1 1 0 1
36 1 45 4 0 0 0 1 0 0 4746 0 1 4 0 1 0 0 0 2 0 0 25 0 0 1 2 1 1 0 0 0
37 3 48 4 0 0 0 0 1 0 6110 0 2 1 0 1 0 0 0 3 0 1 31 1 0 0 1 2 1 1 0 1
38 2 18 2 0 0 0 1 0 0 2100 0 2 4 0 1 0 1 0 2 1 0 37 1 0 1 1 2 1 0 0 0
39 2 10 2 0 0 0 0 0 0 1225 0 2 2 0 1 0 0 0 2 0 0 37 0 0 1 1 2 1 1 0 1
40 1 9 2 0 0 0 1 0 0 458 0 2 4 0 1 0 0 0 3 1 0 24 0 0 1 1 2 1 0 0 1
41 3 30 2 0 0 0 1 0 0 2333 2 4 4 0 1 0 0 0 2 0 0 30 1 0 1 1 3 1 0 0 1
42 1 12 2 0 0 0 1 0 0 1158 2 2 3 1 0 0 0 0 1 0 0 26 0 0 1 1 2 1 1 0 1
43 1 18 3 0 0 0 0 0 0 6204 0 2 2 0 1 0 0 0 4 1 0 44 0 0 1 1 1 2 1 0 1
44 0 30 4 0 1 0 0 0 0 6187 1 3 1 0 0 1 0 0 4 0 0 24 0 1 0 2 2 1 0 0 1
45 0 48 4 0 1 0 0 0 0 6143 0 4 4 0 0 0 0 0 4 0 1 58 1 0 0 2 1 1 0 0 0
46 3 11 4 1 0 0 0 0 0 1393 0 1 4 0 0 0 0 0 4 0 0 35 0 0 1 2 3 1 0 0 1
47 3 36 2 0 0 0 1 0 0 2299 2 4 4 0 1 0 0 0 4 0 0 39 0 0 1 1 2 1 0 0 1
48 0 6 2 0 1 0 0 0 0 1352 2 0 1 0 0 0 0 0 2 0 0 23 0 1 0 1 0 1 1 0 1
49 3 11 4 1 0 0 0 0 0 7228 0 2 1 0 1 0 0 0 4 0 0 39 0 0 1 2 1 1 0 0 1
50 3 12 2 0 0 0 1 0 0 2073 1 2 4 0 0 0 1 0 2 1 0 28 0 0 1 1 2 1 0 0 1
51 1 24 3 0 0 1 0 0 0 2333 4 1 4 0 1 0 0 0 2 0 0 29 1 0 1 1 1 1 0 0 1
52 1 27 3 0 1 0 0 0 0 5965 0 4 1 0 1 0 0 0 2 0 0 30 0 0 1 2 3 1 1 0 1
53 3 12 2 0 0 0 1 0 0 1262 0 2 3 0 1 0 0 0 2 0 0 25 0 0 1 1 2 1 0 0 1
54 3 18 2 0 1 0 0 0 0 3378 4 2 2 0 1 0 0 0 1 0 0 31 0 0 1 1 2 1 1 0 1
55 1 36 3 1 0 0 0 0 0 2225 0 4 4 0 1 0 0 0 4 0 1 57 1 0 0 2 2 1 1 0 0
56 3 6 1 1 0 0 0 0 0 783 4 2 1 0 1 0 0 1 2 1 0 26 1 0 1 1 1 2 0 0 1
57 1 12 2 0 0 0 1 0 0 6468 4 0 2 0 1 0 0 0 1 0 1 52 0 0 1 1 3 1 1 0 0
58 3 36 4 0 0 0 1 0 0 9566 0 2 2 0 0 0 0 0 2 0 0 31 1 0 1 2 2 1 0 0 1
59 2 18 2 1 0 0 0 0 0 1961 0 4 3 0 0 0 0 0 2 0 0 23 0 0 1 1 3 1 0 0 1
60 0 36 4 0 0 1 0 0 0 6229 0 1 4 0 0 0 1 0 4 0 1 23 0 1 0 2 1 1 1 0 0
61 1 9 2 0 0 0 0 0 1 1391 0 2 2 0 0 1 0 0 1 1 0 27 1 0 1 1 2 1 1 0 1
62 1 15 4 0 0 0 1 0 0 1537 4 4 4 0 1 0 0 1 4 1 0 50 0 0 1 2 2 1 1 0 1
63 1 36 0 0 0 0 0 0 1 1953 0 4 4 0 1 0 0 0 4 0 1 61 0 0 0 1 3 1 1 0 0
64 1 48 0 0 0 0 0 0 1 14421 0 2 2 0 1 0 0 0 2 0 0 25 0 0 1 1 2 1 1 0 0
65 3 24 2 0 0 0 1 0 0 3181 0 1 4 0 0 0 0 0 4 0 0 26 0 0 1 1 2 1 1 0 1
66 3 27 2 0 0 0 0 0 0 5190 4 4 4 0 1 0 0 0 4 0 0 48 0 0 1 4 2 2 1 0 1
67 3 12 2 0 0 0 1 0 0 2171 0 1 2 0 0 0 0 0 2 0 0 29 1 0 1 1 2 1 0 0 1
68 1 12 2 1 0 0 0 0 0 1007 3 2 4 0 0 1 0 0 1 1 0 22 0 0 1 1 2 1 0 0 1
69 3 36 2 0 0 0 0 1 0 1819 0 2 4 0 1 0 0 0 4 0 1 37 1 0 0 1 2 1 1 0 0
70 3 36 2 0 0 0 1 0 0 2394 4 2 4 0 0 0 0 0 4 0 0 25 0 0 1 1 2 1 0 0 1
71 3 36 2 0 1 0 0 0 0 8133 0 2 1 0 0 0 0 0 2 0 0 30 1 0 1 1 2 1 0 0 1
72 3 7 4 0 0 0 1 0 0 730 4 4 4 0 1 0 0 0 2 0 0 46 0 1 0 2 1 1 1 0 1
73 0 8 4 0 0 0 0 0 0 1164 0 4 3 0 1 0 0 0 4 0 1 51 1 0 0 2 3 2 1 0 1
74 1 42 4 0 0 0 0 0 1 5954 0 3 2 0 0 0 0 0 1 1 0 41 1 0 1 2 1 1 0 0 1
75 0 36 2 0 0 0 0 1 0 1977 4 4 4 0 1 0 0 0 4 0 1 40 0 0 1 1 3 1 1 0 0
76 0 12 4 0 1 0 0 0 0 1526 0 4 4 0 1 0 0 0 4 0 1 66 0 0 0 2 3 1 0 0 1
77 0 42 2 0 0 0 1 0 0 3965 0 1 4 0 1 0 0 0 3 0 0 34 0 0 1 1 2 1 0 0 0
78 1 11 3 0 0 0 1 0 0 4771 0 3 2 0 1 0 0 0 4 0 0 51 0 0 1 1 2 1 0 0 1
79 3 54 0 0 1 0 0 0 0 9436 4 2 2 0 1 0 0 0 2 0 0 39 0 0 1 1 1 2 0 0 1
80 1 30 2 0 0 1 0 0 0 3832 0 1 2 0 0 1 0 0 1 0 0 22 0 0 1 1 2 1 0 0 1
81 3 24 2 0 0 0 1 0 0 5943 4 1 1 0 0 0 0 0 1 0 0 44 0 0 1 2 2 1 1 0 0
82 3 15 2 0 0 0 1 0 0 1213 2 4 4 0 1 0 0 0 3 0 0 47 1 0 1 1 2 1 1 0 1
83 3 18 2 0 0 0 0 0 1 1568 1 2 3 0 0 0 0 0 4 0 0 24 0 1 0 1 1 1 0 0 1
84 0 24 2 0 0 0 0 0 0 1755 0 4 4 0 0 0 0 1 4 1 0 58 0 0 1 1 1 1 1 0 1
85 0 10 2 0 0 0 1 0 0 2315 0 4 3 0 1 0 0 0 4 1 0 52 0 0 1 1 1 1 0 0 1
86 3 12 4 0 0 0 0 0 1 1412 0 2 4 0 0 0 0 1 2 1 0 29 0 0 1 2 3 1 1 0 1
87 1 18 4 0 0 1 0 0 0 1295 0 1 4 0 0 0 0 0 1 0 0 27 0 0 1 2 2 1 0 0 1
88 1 36 2 0 0 0 0 1 0 12612 1 2 1 0 1 0 0 0 4 0 1 47 0 0 0 1 2 2 1 0 0
89 0 18 2 1 0 0 0 0 0 2249 1 3 4 0 1 0 0 0 3 0 0 30 0 0 1 1 3 2 1 0 1
90 0 12 0 0 0 0 0 0 0 1108 0 3 4 0 1 0 0 0 3 1 0 28 0 0 1 2 2 1 0 0 0
91 3 12 4 0 0 0 1 0 0 618 0 4 4 0 1 0 0 0 4 1 0 56 0 0 1 1 2 1 0 0 1
92 0 12 4 0 1 0 0 0 0 1409 0 4 4 0 1 0 0 0 3 1 0 54 0 0 1 1 2 1 0 0 1
93 3 12 4 0 0 0 1 0 0 797 4 4 4 0 0 0 0 0 3 0 0 33 1 0 1 1 1 2 0 0 0
94 2 24 4 0 0 1 0 0 0 3617 4 4 4 0 1 0 1 0 4 0 1 20 0 1 0 2 2 1 0 0 1
95 1 12 2 1 0 0 0 0 0 1318 3 4 4 0 1 0 0 0 4 1 0 54 0 0 1 1 2 1 1 0 1
96 1 54 0 0 0 0 0 0 1 15945 0 1 3 0 1 0 0 0 4 0 1 58 0 1 0 1 2 1 1 0 0
97 3 12 4 0 0 0 0 1 0 2012 4 3 4 0 0 0 0 0 2 0 0 61 0 0 1 1 2 1 0 0 1
98 1 18 2 0 0 0 0 0 1 2622 1 2 4 0 1 0 0 0 4 0 0 34 0 0 1 1 2 1 0 0 1
99 1 36 4 0 0 0 1 0 0 2337 0 4 4 0 1 0 0 0 4 1 0 36 0 0 1 1 2 1 0 0 1
100 1 20 3 0 1 0 0 0 0 7057 4 3 3 0 1 0 0 0 4 0 0 36 1 1 0 2 3 2 1 0 1
101 3 24 2 1 0 0 0 0 0 1469 1 4 4 0 0 1 0 0 4 1 0 41 0 1 0 1 1 1 0 0 1
102 1 36 2 0 0 0 1 0 0 2323 0 3 4 0 1 0 0 0 4 0 0 24 0 1 0 1 2 1 0 0 1
103 3 6 3 0 0 0 1 0 0 932 0 2 3 0 0 0 0 0 2 1 0 24 0 0 1 1 2 1 0 0 1
104 1 9 4 0 0 1 0 0 0 1919 0 3 4 0 1 0 0 0 3 0 0 35 0 1 0 1 2 1 1 0 1
105 3 12 2 0 1 0 0 0 0 2445 4 1 2 0 0 1 0 0 4 0 0 26 0 1 0 1 2 1 1 0 1
106 1 24 4 0 0 0 0 0 0 11938 0 2 2 0 1 0 1 0 3 0 0 39 0 0 1 2 3 2 1 0 0
107 3 18 1 1 0 0 0 0 0 6458 0 4 2 0 1 0 0 0 4 0 1 39 1 0 1 2 3 2 1 0 0
108 1 12 2 1 0 0 0 0 0 6078 0 3 2 0 1 0 0 0 2 0 0 32 0 0 1 1 2 1 0 0 1
109 0 24 2 0 0 1 0 0 0 7721 4 1 1 0 0 0 0 0 2 0 0 30 0 0 1 1 2 1 1 1 1
110 1 14 2 0 0 0 0 0 1 1410 2 4 1 0 0 1 0 0 2 1 0 35 0 0 1 1 2 1 1 0 1
111 1 6 3 0 0 0 0 0 1 1449 1 4 1 1 0 0 0 0 2 0 0 31 1 0 1 2 2 2 0 0 1
112 2 15 2 0 0 0 0 1 0 392 0 1 4 0 0 0 0 0 4 0 0 23 0 1 0 1 2 1 1 0 1
113 1 18 2 1 0 0 0 0 0 6260 0 3 3 0 1 0 0 0 3 1 0 28 0 1 0 1 1 1 0 0 1
114 3 36 4 1 0 0 0 0 0 7855 0 2 4 0 0 0 0 0 2 1 0 25 1 0 1 2 2 1 1 0 0
115 0 12 2 0 0 0 1 0 0 1680 2 4 3 0 0 1 0 0 1 1 0 35 0 0 1 1 2 1 0 0 1
116 3 48 4 0 0 0 1 0 0 3578 4 4 4 0 1 0 0 0 1 1 0 47 0 0 1 1 2 1 1 0 1
117 0 42 2 0 0 0 1 0 0 7174 4 3 4 0 0 0 0 0 3 0 0 30 0 0 1 1 3 1 1 0 0
118 0 10 4 0 0 1 0 0 0 2132 4 1 2 0 0 0 1 0 3 1 0 27 0 1 0 2 2 1 0 1 1
119 0 33 4 0 0 1 0 0 0 4281 2 2 1 0 0 0 0 0 4 0 0 23 0 0 1 2 2 1 0 0 0
120 1 12 4 1 0 0 0 0 0 2366 2 3 3 1 0 0 0 0 3 0 0 36 0 0 1 1 3 1 1 0 1
121 0 21 2 0 0 0 1 0 0 1835 0 2 3 0 0 0 0 0 2 1 0 25 0 0 1 2 2 1 1 0 0
122 3 24 4 0 1 0 0 0 0 3868 0 4 4 0 0 0 0 0 2 0 0 41 0 1 0 2 3 1 1 0 1
123 3 12 2 0 0 1 0 0 0 1768 0 2 3 0 1 0 0 0 2 1 0 24 0 1 0 1 1 1 0 0 1
124 2 10 4 1 0 0 0 0 0 781 0 4 4 0 1 0 0 0 4 0 1 63 0 0 0 2 2 1 1 0 1
125 1 18 2 0 0 1 0 0 0 1924 4 1 4 0 0 0 0 0 3 1 0 27 0 1 0 1 2 1 0 0 0
126 0 12 4 1 0 0 0 0 0 2121 0 2 4 0 1 0 0 0 2 0 0 30 0 0 1 2 2 1 0 0 1
127 0 12 2 0 0 0 1 0 0 701 0 2 4 0 0 1 0 0 2 1 0 40 0 0 1 1 1 1 0 0 1
128 1 12 2 0 0 0 0 0 0 639 0 2 4 0 1 0 0 0 2 0 0 30 0 0 1 1 2 1 0 0 0
129 1 12 4 0 1 0 0 0 0 1860 0 0 4 0 1 0 0 0 2 0 0 34 0 0 1 2 3 1 1 0 1
130 0 12 4 1 0 0 0 0 0 3499 0 2 3 0 0 0 1 0 2 1 0 29 0 0 1 2 2 1 0 0 0
131 1 48 2 1 0 0 0 0 0 8487 4 3 1 0 0 0 0 0 2 0 0 24 0 0 1 1 2 1 0 0 1
132 0 36 3 0 0 0 0 1 0 6887 0 2 4 0 1 0 0 0 3 0 0 29 1 0 1 1 2 1 1 0 0
133 3 15 2 0 0 1 0 0 0 2708 0 1 2 0 1 0 0 0 3 0 0 27 1 0 1 2 1 1 0 0 1
134 3 18 2 0 0 1 0 0 0 1984 0 2 4 0 1 0 0 0 4 0 1 47 1 0 0 2 2 1 0 0 1
135 3 60 2 0 0 0 1 0 0 10144 1 3 2 0 0 0 0 0 4 1 0 21 0 0 1 1 2 1 1 0 1
136 3 12 4 0 0 0 1 0 0 1240 4 4 4 0 0 0 0 0 2 1 0 38 0 0 1 2 2 1 1 0 1
137 3 27 3 0 1 0 0 0 0 8613 3 2 2 0 1 0 0 0 2 0 0 27 0 0 1 2 2 1 0 0 1
138 1 12 2 0 0 0 1 0 0 766 2 2 4 0 1 0 0 0 3 1 0 66 0 0 1 1 1 1 0 0 0
139 1 15 4 0 0 0 1 0 0 2728 4 3 4 0 1 0 0 1 2 1 0 35 1 0 1 3 2 1 1 0 1
140 2 12 2 0 0 0 1 0 0 1881 0 2 2 0 0 0 0 0 2 0 0 44 0 1 0 1 1 1 1 0 1
141 2 6 2 1 0 0 0 0 0 709 3 1 2 0 0 1 0 0 2 1 0 27 0 0 1 1 0 1 0 1 1
142 1 36 2 0 0 0 1 0 0 4795 0 1 4 0 0 0 0 0 1 0 1 30 0 0 1 1 3 1 1 0 1
143 0 27 2 0 0 0 1 0 0 3416 0 2 3 0 1 0 0 0 2 0 0 27 0 0 1 1 3 1 0 0 1
144 0 18 2 0 0 1 0 0 0 2462 0 2 2 0 1 0 0 0 2 0 0 22 0 0 1 1 2 1 0 0 0
145 3 21 4 0 0 1 0 0 0 2288 0 1 4 0 0 0 0 0 4 0 0 23 0 0 1 1 2 1 1 0 1
146 1 48 1 0 0 0 0 0 1 3566 1 3 4 0 1 0 0 0 2 0 0 30 0 0 1 1 2 1 0 0 1
147 0 6 4 1 0 0 0 0 0 860 0 4 1 0 0 0 0 0 4 0 1 39 0 0 1 2 2 1 1 0 1
148 3 12 4 1 0 0 0 0 0 682 1 3 4 0 0 0 0 0 3 0 0 51 0 0 1 2 2 1 1 0 1
149 0 36 4 0 0 1 0 0 0 5371 0 2 3 0 1 0 0 1 2 0 0 28 0 0 1 2 2 1 0 0 1
150 3 18 4 0 0 0 1 0 0 1582 3 4 4 0 1 0 0 0 4 0 0 46 0 0 1 2 2 1 0 0 1
151 3 6 2 0 0 0 1 0 0 1346 1 4 2 0 1 0 0 0 4 0 1 42 1 0 0 1 2 2 1 0 1
152 3 10 2 0 0 0 1 0 0 1924 0 2 1 0 1 0 0 0 4 0 0 38 0 0 1 1 2 1 1 1 1
153 2 36 2 0 0 0 1 0 0 5848 0 2 4 0 1 0 0 0 1 0 0 24 0 0 1 1 2 1 0 0 1
154 1 24 4 0 1 0 0 0 0 7758 3 4 2 0 0 0 0 0 4 0 1 29 0 1 0 1 2 1 0 0 1
155 1 24 3 0 0 0 0 0 1 6967 1 3 4 0 1 0 0 0 4 0 0 36 0 1 0 1 3 1 1 0 1
156 0 12 2 0 0 1 0 0 0 1282 0 2 2 0 0 0 0 0 4 0 0 20 0 1 0 1 2 1 0 0 0
157 0 9 4 0 0 0 0 0 0 1288 1 4 3 0 1 0 0 1 4 1 0 48 0 0 1 2 2 2 0 1 1
158 0 12 1 0 0 0 0 0 0 339 0 4 4 0 0 1 0 0 1 0 0 45 1 0 1 1 1 1 0 0 1
159 1 24 2 1 0 0 0 0 0 3512 1 3 2 0 1 0 0 0 3 0 0 38 1 0 1 2 2 1 1 0 1
160 3 6 4 0 0 0 1 0 0 1898 4 2 1 0 1 0 0 0 2 1 0 34 0 0 1 2 1 2 0 0 1
161 3 24 4 0 0 0 1 0 0 2872 1 4 3 0 1 0 0 0 4 1 0 36 0 0 1 1 2 2 1 0 1
162 3 18 4 1 0 0 0 0 0 1055 0 1 4 0 0 0 0 0 1 0 0 30 0 0 1 2 2 1 0 0 1
163 3 15 2 0 0 0 0 0 0 1262 2 3 4 0 1 0 0 0 3 0 0 36 0 0 1 2 2 1 1 0 1
164 1 10 2 1 0 0 0 0 0 7308 0 0 2 0 1 0 0 0 4 0 1 70 1 0 0 1 3 1 1 0 1
165 3 36 2 1 0 0 0 0 0 909 2 4 4 0 1 0 0 0 4 0 0 36 0 0 1 1 2 1 0 0 1
166 3 6 2 0 0 1 0 0 0 2978 2 2 1 0 1 0 0 0 2 0 0 32 0 0 1 1 2 1 1 0 1
167 0 18 2 0 0 1 0 0 0 1131 0 0 4 0 0 0 0 0 2 0 0 33 0 0 1 1 2 1 0 0 0
168 1 11 2 0 0 1 0 0 0 1577 3 1 4 0 0 0 0 0 1 1 0 20 0 0 1 1 2 1 0 0 1
169 3 24 2 0 0 1 0 0 0 3972 0 3 2 0 0 0 0 0 4 0 0 25 0 1 0 1 2 1 1 0 1
170 1 24 4 0 0 0 0 0 1 1935 0 4 4 1 0 0 0 0 4 1 0 31 0 0 1 2 2 1 1 0 0
171 0 15 0 1 0 0 0 0 0 950 0 4 4 0 1 0 0 0 3 0 0 33 0 1 0 2 2 2 0 0 0
172 3 12 2 0 0 1 0 0 0 763 0 2 4 0 0 0 0 0 1 1 0 26 0 0 1 1 2 1 1 0 1
173 1 24 3 0 0 1 0 0 0 2064 0 0 3 0 0 0 0 0 2 0 0 34 0 0 1 1 3 1 1 0 0
174 1 8 2 0 0 0 1 0 0 1414 0 2 4 0 1 0 0 1 2 1 0 33 0 0 1 1 2 1 0 1 1
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PFBM_Output

Data Mining: Find Best Model Prediction Date: 13-Dec-2021 05:34:55
Output Navigator Elapsed Times in Milliseconds
Inputs Model Performance: Training PMML Model Training: Prediction Summary Data Reading Time Algorithm Time Report Time Total
114 5824 1004 6942
Inputs
Data
Workbook GermanCredit-1.xls
Worksheet Data
Data Range $A$1:$AF$1001
# Records 1000
Variables
# Variables 31
Scale Variables OBS# CHK_ACCT DURATION HISTORY USED_CAR FURNITURE RADIO/TV EDUCATION RETRAINING AMOUNT SAV_ACCT EMPLOYMENT INSTALL_RATE MALE_DIV MALE_SINGLE MALE_MAR_or_WID CO-APPLICANT GUARANTOR PRESENT_RESIDENT REAL_ESTATE PROP_UNKN_NONE AGE OTHER_INSTALL RENT OWN_RES NUM_CREDITS JOB NUM_DEPENDENTS TELEPHONE FOREIGN RESPONSE
Categorical Variables
Output Variable NEW_CAR
Rescaling: Fitting Parameters
Rescale Data?
Find Best Model: Fitting Parameters
Learners LINEAR_REGRESSION, NEAREST_NEIGHBORS, DECISION_TREE, NEURAL_NETWORK, BAGGING, BOOSTING, RANDOM_TREES
Find Best Model Prediction: Model Parameters
Metric for scoring R2
Based on partition Training
Linear Regression
Regression Model: Fitting Parameters
Fit Intercept
Nearest Neighbors
Nearest Neighbors: Fitting Parameters
# Nearest neighbors (K) 1
Decision Tree
Decision Tree: Model Parameters
Prune?
Scoring tree type Fully grown
Neural Network
Neural Network: Fitting Parameters
Random seed for initial weights 12345
# Hidden Layers 0
Learning rate 0.1
Weight change momentum 0.6
Error tolerance 0.01
Weight decay 0
Cost function Sum of Squares
Hidden layer activation function LOGISTIC SIGMOID
Output layer activation function LOGISTIC SIGMOID
Learning order Original
Response correction 0.01
Data for error computation TRAINING ONLY
Maximum number of epochs 30
Maximum number of epochs without improvement 5
Maximum training time 3600
Minimum relative change in error 0.0001
Minimum relative change in error compared to null model 0.001
Bagging
Ensemble Parameters
Weak learner Regression Tree
Number of weak learners 10
Show weak learner models?
Bagging Parameters
Bootstrap seed 12345
Boosting

Ensemble Parameters
Weak learner Regression Tree
Number of weak learners 10
Show weak learner models?

Boosting Regression: Fitting Parameters
Step size 0.3
Random Trees

Ensemble Parameters
Weak learner Regression Tree
Number of weak learners 10
Show weak learner models?
Bagging Parameters
Bootstrap seed 12345

Random Trees: Fitting Parameters
# Selected features 6
Feature selection random seed 12345
Output Options
Summary report of scoring on training data
Note: Scoring will be done using Nearest Neighbors
Model Performance: Training
Metric SSE MSE RMSE MAD R2
Linear Regression 42.897637968 0.042897638 0.2071174497 0.1067816972 0.7606746225
Decision Tree 9.4089285714 0.0094089286 0.0969996318 0.0188178571 0.947507707
Nearest Neighbors 0 0 0 0 1
Neural Network 781.42 0.78142 0.8839796378 0.776 -3.3595322577
Bagging 9.64 0.00964 0.0981835017 0.0368 0.9462185624
Boosting 10.7265420354 0.010726542 0.1035690206 0.0498431608 0.9401567582
Random Trees 9.64 0.00964 0.0981835017 0.0368 0.9462185624

Inputs

Model Performance: Training

PMML Model

Training: Prediction Summary

PFBM_TrainingScore

Data Mining: Find Best Model Prediction – Prediction of Training Data Date: 13-Dec-2021 05:34:55

Output Navigator Elapsed Times in Milliseconds
Inputs Model Performance: Training PMML Model Training: Prediction Summary Data Reading Time Algorithm Time Report Time Total
114 5824 1004 6942

Training: Prediction Summary
Metric Value
SSE 0
MSE 0
RMSE 0
MAD 0
R2 1

Inputs

Model Performance: Training

PMML Model

Training: Prediction Summary

PFBM_Stored

Data Mining: Find Best Model Prediction – Stored Model Date: 13-Dec-2021 05:34:55

Output Navigator Elapsed Times in Milliseconds
Inputs Model Performance: Training PMML Model Training: Prediction Summary Data Reading Time Algorithm Time Report Time Total
114 5824 1004 6942

PMML Model




2021-12-13 5:34:54

















































































































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Inputs

Model Performance: Training

PMML Model

Training: Prediction Summary

STDPartition

Data Mining: Standard Partitioner Date: 13-Dec-2021 05:15:44 {“comment”:”this RASON template was auto-generated by Analytic Solver Data Mining”,”datasources”:{},”datasets”:{},”transformer”:{“partitioner”:{“type”:”transformation”,”algorithm”:”partitioning”,”parameters”:{“partitionMethod”:”RANDOM”,”ratios”:[[“Training”,0.59999999999999998],[“Validation”,0.40000000000000002]],”seed”:12345}}},”actions”:{}}
Output Navigator Elapsed Times in Milliseconds
Inputs Partition Summary Partitioned Data Data Reading Time Algorithm Time Report Time Total
315 97 216 628

Inputs
Data

Workbook GermanCredit-1.xls
Worksheet Data
Range $A$1:$AF$1001
# Records in the input data 1000

Variables

# Selected Variables 32
Selected Variables OBS# CHK_ACCT DURATION HISTORY NEW_CAR USED_CAR FURNITURE RADIO/TV EDUCATION RETRAINING AMOUNT SAV_ACCT EMPLOYMENT INSTALL_RATE MALE_DIV MALE_SINGLE MALE_MAR_or_WID CO-APPLICANT GUARANTOR PRESENT_RESIDENT REAL_ESTATE PROP_UNKN_NONE AGE OTHER_INSTALL RENT OWN_RES NUM_CREDITS JOB NUM_DEPENDENTS TELEPHONE FOREIGN RESPONSE
Partitioning Parameters
Partitioning type RANDOM
Random seed 12345
Ratio – Training 0.6
Ratio – Validation 0.4
Partition Summary
Partition # Records
Training 600
Validation 400
Partitioned Data
Record ID OBS# CHK_ACCT DURATION HISTORY NEW_CAR USED_CAR FURNITURE RADIO/TV EDUCATION RETRAINING AMOUNT SAV_ACCT EMPLOYMENT INSTALL_RATE MALE_DIV MALE_SINGLE MALE_MAR_or_WID CO-APPLICANT GUARANTOR PRESENT_RESIDENT REAL_ESTATE PROP_UNKN_NONE AGE OTHER_INSTALL RENT OWN_RES NUM_CREDITS JOB NUM_DEPENDENTS TELEPHONE FOREIGN RESPONSE
Record 1 1 0 6 4 0 0 0 1 0 0 1169 4 4 4 0 1 0 0 0 4 1 0 67 0 0 1 2 2 1 1 0 1
Record 5 5 0 24 3 1 0 0 0 0 0 4870 0 2 3 0 1 0 0 0 4 0 1 53 0 0 0 2 2 2 0 0 0
Record 8 8 1 36 2 0 1 0 0 0 0 6948 0 2 2 0 1 0 0 0 2 0 0 35 0 1 0 1 3 1 1 0 1
Record 11 11 1 12 2 1 0 0 0 0 0 1295 0 1 3 0 0 0 0 0 1 0 0 25 0 1 0 1 2 1 0 0 0
Record 12 12 0 48 2 0 0 0 0 0 1 4308 0 1 3 0 0 0 0 0 4 0 0 24 0 1 0 1 2 1 0 0 0
Record 15 15 0 15 2 1 0 0 0 0 0 1403 0 2 2 0 0 0 0 0 4 0 0 28 0 1 0 1 2 1 0 0 1
Record 16 16 0 24 2 0 0 0 1 0 0 1282 1 2 4 0 0 0 0 0 2 0 0 32 0 0 1 1 1 1 0 0 0
Record 18 18 0 30 0 0 0 0 0 0 1 8072 4 1 2 0 1 0 0 0 3 0 0 25 1 0 1 3 2 1 0 0 1
Record 20 20 3 24 2 0 0 0 1 0 0 3430 2 4 3 0 1 0 0 0 2 0 0 31 0 0 1 1 2 2 1 0 1
Record 21 21 3 9 4 1 0 0 0 0 0 2134 0 2 4 0 1 0 0 0 4 0 0 48 0 0 1 3 2 1 1 0 1
Record 22 22 0 6 2 0 0 0 1 0 0 2647 2 2 2 0 1 0 0 0 3 1 0 44 0 1 0 1 2 2 0 0 1
Record 23 23 0 10 4 1 0 0 0 0 0 2241 0 1 1 0 1 0 0 0 3 1 0 48 0 1 0 2 1 2 0 1 1
Record 24 24 1 12 4 0 1 0 0 0 0 1804 1 1 3 0 1 0 0 0 4 0 0 44 0 0 1 1 2 1 0 0 1
Record 25 25 3 10 4 0 0 1 0 0 0 2069 4 2 2 0 0 1 0 0 1 0 0 26 0 0 1 2 2 1 0 1 1
Record 26 26 0 6 2 0 0 1 0 0 0 1374 0 2 1 0 1 0 0 0 2 1 0 36 1 0 1 1 1 1 1 0 1
Record 28 28 2 12 1 0 0 0 1 0 0 409 3 2 3 0 0 0 0 0 3 1 0 42 0 1 0 2 2 1 0 0 1
Record 29 29 1 7 2 0 0 0 1 0 0 2415 0 2 3 0 1 0 0 1 2 1 0 34 0 0 1 1 2 1 0 0 1
Record 32 32 0 24 2 0 0 1 0 0 0 4020 0 2 2 0 1 0 0 0 2 0 0 27 1 0 1 1 2 1 0 0 1
Record 33 33 1 18 2 1 0 0 0 0 0 5866 1 2 2 0 1 0 0 0 2 0 0 30 0 0 1 2 2 1 1 0 1
Record 35 35 2 12 2 0 0 1 0 0 0 1474 0 1 4 0 0 0 0 0 1 0 0 33 1 0 1 1 3 1 1 0 1
Record 37 37 3 48 4 0 0 0 0 1 0 6110 0 2 1 0 1 0 0 0 3 0 1 31 1 0 0 1 2 1 1 0 1
Record 39 39 2 10 2 0 0 0 0 0 0 1225 0 2 2 0 1 0 0 0 2 0 0 37 0 0 1 1 2 1 1 0 1
Record 41 41 3 30 2 0 0 0 1 0 0 2333 2 4 4 0 1 0 0 0 2 0 0 30 1 0 1 1 3 1 0 0 1
Record 42 42 1 12 2 0 0 0 1 0 0 1158 2 2 3 1 0 0 0 0 1 0 0 26 0 0 1 1 2 1 1 0 1
Record 45 45 0 48 4 0 1 0 0 0 0 6143 0 4 4 0 0 0 0 0 4 0 1 58 1 0 0 2 1 1 0 0 0
Record 47 47 3 36 2 0 0 0 1 0 0 2299 2 4 4 0 1 0 0 0 4 0 0 39 0 0 1 1 2 1 0 0 1
Record 48 48 0 6 2 0 1 0 0 0 0 1352 2 0 1 0 0 0 0 0 2 0 0 23 0 1 0 1 0 1 1 0 1
Record 49 49 3 11 4 1 0 0 0 0 0 7228 0 2 1 0 1 0 0 0 4 0 0 39 0 0 1 2 1 1 0 0 1
Record 54 54 3 18 2 0 1 0 0 0 0 3378 4 2 2 0 1 0 0 0 1 0 0 31 0 0 1 1 2 1 1 0 1
Record 56 56 3 6 1 1 0 0 0 0 0 783 4 2 1 0 1 0 0 1 2 1 0 26 1 0 1 1 1 2 0 0 1
Record 57 57 1 12 2 0 0 0 1 0 0 6468 4 0 2 0 1 0 0 0 1 0 1 52 0 0 1 1 3 1 1 0 0
Record 59 59 2 18 2 1 0 0 0 0 0 1961 0 4 3 0 0 0 0 0 2 0 0 23 0 0 1 1 3 1 0 0 1
Record 60 60 0 36 4 0 0 1 0 0 0 6229 0 1 4 0 0 0 1 0 4 0 1 23 0 1 0 2 1 1 1 0 0
Record 61 61 1 9 2 0 0 0 0 0 1 1391 0 2 2 0 0 1 0 0 1 1 0 27 1 0 1 1 2 1 1 0 1
Record 62 62 1 15 4 0 0 0 1 0 0 1537 4 4 4 0 1 0 0 1 4 1 0 50 0 0 1 2 2 1 1 0 1
Record 65 65 3 24 2 0 0 0 1 0 0 3181 0 1 4 0 0 0 0 0 4 0 0 26 0 0 1 1 2 1 1 0 1
Record 66 66 3 27 2 0 0 0 0 0 0 5190 4 4 4 0 1 0 0 0 4 0 0 48 0 0 1 4 2 2 1 0 1
Record 68 68 1 12 2 1 0 0 0 0 0 1007 3 2 4 0 0 1 0 0 1 1 0 22 0 0 1 1 2 1 0 0 1
Record 71 71 3 36 2 0 1 0 0 0 0 8133 0 2 1 0 0 0 0 0 2 0 0 30 1 0 1 1 2 1 0 0 1
Record 73 73 0 8 4 0 0 0 0 0 0 1164 0 4 3 0 1 0 0 0 4 0 1 51 1 0 0 2 3 2 1 0 1
Record 76 76 0 12 4 0 1 0 0 0 0 1526 0 4 4 0 1 0 0 0 4 0 1 66 0 0 0 2 3 1 0 0 1
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Inputs

Partition Summary

Partitioned Data

CT_Output

1

Data Mining: Classification Tree Date: 13-Dec-2021 05:58:14 {“comment”:”this RASON template was auto-generated by Analytic Solver Data Mining”,”datasources”:{},”datasets”:{},”estimator”:{“treeClassificationEstimator”:{“type”:”classification”,”algorithm”:”decisionTree”,”parameters”:{“categoricalFeaturesNames”:[],”priorProbMethod”:”EMPIRICAL”}}},”actions”:{}}

Output Navigator Elapsed Times in Milliseconds

Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total
100 122 67 289

Inputs
Data
Workbook GermanCredit-1.xls

Worksheet STDPartition
Training data used for building the model $C$37:$AI$636
# Records in the training data 600
Validation data $C$637:$AI$1036
# Records in the validation data 400

Variables

# Variables 2
Scale Variables OBS# AMOUNT

Categorical Variables

Output Variable EDUCATION

Rescaling: Fitting Parameters
Rescale Data?

Decision Tree Classification: Fitting Parameters
Prior Probability Calculation EMPIRICAL

Decision Tree: Model Parameters
Prune?
Scoring tree type Fully grown

Decision Tree Classification: Model Parameters
# Classes 2
Success Class 1
Success Probability 0.5
Decision Tree: Reporting Parameters
Trees to draw NONE
# Max levels to display 7
Show feature importance?

Output Options
Summary report of scoring on training data

Summary report of scoring on validation data
Training Log (Growing the full tree using training data) Prune Log (Using Validation Data)
# Decision Nodes Error Rate

# Decision Nodes Error Rate
0 0.0583333333 0 0.0925
1 0.0583333333 1 0.0925
2 0.0583333333 2 0.0925
3 0.0583333333 3 0.0375
4 0.0583333333 4 0.0375
5 0.0583333333 5 0.0375
6 0.0533333333 6 0.0375
7 0.0533333333 7 0.0375
8 0.0533333333 8 0.0375
9 0.0516666667 9 0.0375
10 0.05 10 0.0375
11 0.05 11 0.0375
12 0.0483333333 12 0.0375
13 0.0466666667 13 0.0375
14 0.045 14 0.0375
15 0.0433333333 15 0.0375
16 0.0433333333 16 0.065
17 0.0433333333 17 0.065
18 0.0416666667 18 0.065
19 0.0416666667 19 0.065
20 0.0416666667 20 0.065
21 0.0416666667 21 0.065
22 0.0416666667 22 0.065
23 0.0383333333 23 0.065
24 0.0366666667 24 0.065
25 0.0366666667 25 0.065
26 0.035 26 0.065
27 0.0333333333 27 0.065
28 0.0316666667 28 0.065
29 0.03 29 0.065
30 0.03 30 0.065
31 0.03 31 0.065
32 0.0283333333 32 0.065
33 0.0266666667 33 0.065
34 0.0266666667 34 0.065
35 0.025 35 0.065
36 0.0233333333 36 0.065
37 0.0233333333 37 0.065
38 0.0233333333 38 0.065
39 0.02 39 0.065
40 0.0183333333 40 0.065
41 0.0183333333 41 0.065
42 0.0183333333 42 0.065
43 0.0166666667 43 0.065
44 0.0166666667 44 0.065
45 0.0166666667 45 0.0925
46 0.015 46 0.0925
47 0.0133333333 47 0.0925
48 0.0116666667 48 0.0925
49 0.0116666667 49 0.0925

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

CT_TrainingScore

1

Data Mining: Classification Tree – Prediction of Training Data Date: 13-Dec-2021 05:58:14

Output Navigator Elapsed Times in Milliseconds
Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total
100 122 67 289

Training: Classification Summary
Confusion Matrix
Actual\Predicted 0 1
0 565 0
1 7 28
Error Report
Class # Cases # Errors % Error
0 565 0 0
1 35 7 20
Overall 600 7 1.1666666667
Metrics

Metric Value

Accuracy (#correct) 593
Accuracy (%correct) 98.8333333333
Specificity 1
Sensitivity (Recall) 0.8
Precision 1
F1 score 0.8888888889
Success Class 1
Success Probability 0.5

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

CT_ValidationScore

1

Data Mining: Classification Tree – Prediction of Validation Data Date: 13-Dec-2021 05:58:14

Output Navigator Elapsed Times in Milliseconds
Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total
100 122 67 289

Validation: Classification Summary

Confusion Matrix
Actual\Predicted 0 1

0 363 22
1 15 0

Error Report
Class # Cases # Errors % Error

0 385 22 5.7142857143
1 15 15 100
Overall 400 37 9.25

Metrics
Metric Value

Accuracy (#correct) 363
Accuracy (%correct) 90.75
Specificity 0.9428571429
Sensitivity (Recall) 0
Precision 0
F1 score N/A

Success Class 1
Success Probability 0.5

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

CT_Stored

1

Data Mining: Classification Tree – Stored Model Date: 13-Dec-2021 05:58:14

Output Navigator Elapsed Times in Milliseconds
Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total
100 122 67 289
PMML Model


2021-12-13 5:58:14

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_Output

1

Data Mining: Logistic Regression Date: 13-Dec-2021 05:49:29 {“comment”:”this RASON template was auto-generated by Analytic Solver Data Mining”,”datasources”:{},”datasets”:{},”estimator”:{“logisticRegressionEstimator”:{“type”:”classification”,”algorithm”:”logisticRegression”,”parameters”:{“categoricalFeaturesNames”:[],”fitIntercept”:true,”maxIterations”:50,”priorProbMethod”:”EMPIRICAL”,”successClass”:”1″}}},”actions”:{}}

Output Navigator Elapsed Times in Milliseconds

Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total
PMML Model Training: Classification Summary Validation: Classification Summary 80 81 74 235

Inputs
Data
Workbook GermanCredit-1.xls
Worksheet STDPartition
Training data used for building the model $C$37:$AI$636
# Records in the training data 600
Validation data $C$637:$AI$1036
# Records in the validation data 400
Variables
# Variables 2
Scale Variables OBS# AMOUNT
Categorical Variables
Output Variable EDUCATION
Rescaling: Fitting Parameters
Rescale Data?
Regression Model: Fitting Parameters
Fit Intercept

Logistic Regression: Fitting Parameters
Maximum Number of Iterations 50

Prior Probability Calculation EMPIRICAL

Logistic Regression: Model Parameters

# Classes 2
Success Class 1
Success Probability 0.5

Regression Model: Reporting Parameters
Variance-Covariance Matrix
Multicollinearity Diagnostic
Analysis of Coefficients

Output Options
Summary report of scoring on training data
Summary report of scoring on validation data

Regression Summary

Metric Value

# Iterations Used 4
Residual DF 597
Residual Deviance 266.1574611866
Multiple R2 0.0025135451
Predictor Screening
Predictor Criteria Included
Intercept 10.8169963208
OBS# 10477.8950787371
AMOUNT 103364.98735065
Tolerance for entering the model 0.0000000138
Coefficients
Predictor Estimate
Intercept -2.8791971289
OBS# -0.0001260984
AMOUNT 0.0000475598

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_TrainingScore

1

Data Mining: Logistic Regression – Prediction of Training Data Date: 13-Dec-2021 05:49:29

Output Navigator Elapsed Times in Milliseconds
Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total
PMML Model Training: Classification Summary Validation: Classification Summary 80 81 74 235
Training: Classification Summary
Confusion Matrix
Actual\Predicted 0 1
0 565 0

1 35 0

Error Report
Class # Cases # Errors % Error
0 565 0 0

1 35 35 100
Overall 600 35 5.8333333333

Metrics
Metric Value

Accuracy (#correct) 565
Accuracy (%correct) 94.1666666667

Specificity 1
Sensitivity (Recall) 0

Precision N/A

F1 score N/A
Success Class 1
Success Probability 0.5

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_ValidationScore

1

Data Mining: Logistic Regression – Prediction of Validation Data Date: 13-Dec-2021 05:49:29

Output Navigator Elapsed Times in Milliseconds
Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total
PMML Model Training: Classification Summary Validation: Classification Summary 80 81 74 235
Validation: Classification Summary
Confusion Matrix
Actual\Predicted 0 1

0 385 0

1 15 0
Error Report
Class # Cases # Errors % Error

0 385 0 0

1 15 15 100

Overall 400 15 3.75

Metrics
Metric Value

Accuracy (#correct) 385
Accuracy (%correct) 96.25

Specificity 1
Sensitivity (Recall) 0
Precision N/A
F1 score N/A
Success Class 1
Success Probability 0.5

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_Stored

1

Data Mining: Logistic Regression – Stored Model Date: 13-Dec-2021 05:49:29

Output Navigator Elapsed Times in Milliseconds
Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total
PMML Model Training: Classification Summary Validation: Classification Summary 80 81 74 235
PMML Model


2021-12-13 5:49:29

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

CT_Output

Data Mining: Classification Tree Date: 13-Dec-2021 05:47:52 {“comment”:”this RASON template was auto-generated by Analytic Solver Data Mining”,”datasources”:{},”datasets”:{},”estimator”:{“treeClassificationEstimator”:{“type”:”classification”,”algorithm”:”decisionTree”,”parameters”:{“categoricalFeaturesNames”:[],”priorProbMethod”:”EMPIRICAL”}}},”actions”:{}}

Output Navigator Elapsed Times in Milliseconds
Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total

177 152 98 427

Inputs
Data
Workbook GermanCredit-1.xls
Worksheet STDPartition
Training data used for building the model $C$37:$AI$636
# Records in the training data 600
Validation data $C$637:$AI$1036
# Records in the validation data 400
Variables
# Variables 2
Scale Variables OBS# AMOUNT
Categorical Variables
Output Variable EDUCATION
Rescaling: Fitting Parameters
Rescale Data?
Decision Tree Classification: Fitting Parameters
Prior Probability Calculation EMPIRICAL
Decision Tree: Model Parameters
Prune?
Scoring tree type Fully grown
Decision Tree Classification: Model Parameters
# Classes 2
Success Class 1
Success Probability 0.5
Decision Tree: Reporting Parameters
Trees to draw NONE
# Max levels to display 7
Show feature importance?
Output Options
Summary report of scoring on training data
Summary report of scoring on validation data

Training Log (Growing the full tree using training data)

# Decision Nodes Error Rate

0 0.0583333333
1 0.0583333333
2 0.0583333333
3 0.0583333333
4 0.0583333333
5 0.0583333333
6 0.0533333333
7 0.0533333333
8 0.0533333333
9 0.0516666667
10 0.05
11 0.05
12 0.0483333333
13 0.0466666667
14 0.045
15 0.0433333333
16 0.0433333333
17 0.0433333333
18 0.0416666667
19 0.0416666667
20 0.0416666667
21 0.0416666667
22 0.0416666667
23 0.0383333333
24 0.0366666667
25 0.0366666667
26 0.035
27 0.0333333333
28 0.0316666667
29 0.03
30 0.03
31 0.03
32 0.0283333333
33 0.0266666667
34 0.0266666667
35 0.025
36 0.0233333333
37 0.0233333333
38 0.0233333333
39 0.02
40 0.0183333333
41 0.0183333333
42 0.0183333333
43 0.0166666667
44 0.0166666667
45 0.0166666667
46 0.015
47 0.0133333333
48 0.0116666667
49 0.0116666667

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

CT_TrainingScore

Data Mining: Classification Tree – Prediction of Training Data Date: 13-Dec-2021 05:47:52

Output Navigator Elapsed Times in Milliseconds
Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total
177 152 98 427
Training: Classification Summary
Confusion Matrix
Actual\Predicted 0 1
0 565 0
1 7 28
Error Report
Class # Cases # Errors % Error
0 565 0 0
1 35 7 20
Overall 600 7 1.1666666667
Metrics
Metric Value
Accuracy (#correct) 593
Accuracy (%correct) 98.8333333333
Specificity 1
Sensitivity (Recall) 0.8
Precision 1
F1 score 0.8888888889
Success Class 1
Success Probability 0.5

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

CT_ValidationScore

Data Mining: Classification Tree – Prediction of Validation Data Date: 13-Dec-2021 05:47:52

Output Navigator Elapsed Times in Milliseconds
Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total
177 152 98 427
Validation: Classification Summary
Confusion Matrix
Actual\Predicted 0 1
0 363 22
1 15 0
Error Report
Class # Cases # Errors % Error
0 385 22 5.7142857143
1 15 15 100
Overall 400 37 9.25
Metrics
Metric Value
Accuracy (#correct) 363
Accuracy (%correct) 90.75
Specificity 0.9428571429
Sensitivity (Recall) 0
Precision 0
F1 score N/A
Success Class 1
Success Probability 0.5

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

CT_Stored

Data Mining: Classification Tree – Stored Model Date: 13-Dec-2021 05:47:52

Output Navigator Elapsed Times in Milliseconds
Inputs PMML Model Training: Classification Summary Validation: Classification Summary Data Reading Time Algorithm Time Report Time Total
177 152 98 427
PMML Model



2021-12-13 5:47:52

Inputs

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_Output

Data Mining: Logistic Regression Date: 13-Dec-2021 05:26:06 {“comment”:”this RASON template was auto-generated by Analytic Solver Data Mining”,”datasources”:{},”datasets”:{},”estimator”:{“logisticRegressionEstimator”:{“type”:”classification”,”algorithm”:”logisticRegression”,”parameters”:{“categoricalFeaturesNames”:[],”fitIntercept”:true,”maxIterations”:50,”priorProbMethod”:”EMPIRICAL”,”successClass”:”1″}}},”actions”:{}}

Output Navigator Elapsed Times in Milliseconds
Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total

PMML Model Training: Classification Summary Validation: Classification Summary 91 96 94 281

Inputs
Data
Workbook GermanCredit-1.xls
Worksheet STDPartition
Training data used for building the model $C$37:$AI$636
# Records in the training data 600
Validation data $C$637:$AI$1036
# Records in the validation data 400
Variables
# Variables 2
Scale Variables OBS# AMOUNT
Categorical Variables
Output Variable EDUCATION
Rescaling: Fitting Parameters
Rescale Data?
Regression Model: Fitting Parameters
Fit Intercept
Logistic Regression: Fitting Parameters
Maximum Number of Iterations 50
Prior Probability Calculation EMPIRICAL
Logistic Regression: Model Parameters
# Classes 2
Success Class 1
Success Probability 0.5
Regression Model: Reporting Parameters
Variance-Covariance Matrix
Multicollinearity Diagnostic
Analysis of Coefficients
Output Options
Summary report of scoring on training data
Summary report of scoring on validation data
Regression Summary
Metric Value
# Iterations Used 4
Residual DF 597
Residual Deviance 266.1574611866
Multiple R2 0.0025135451
Predictor Screening
Predictor Criteria Included
Intercept 10.8169963208
OBS# 10477.8950787371
AMOUNT 103364.98735065
Tolerance for entering the model 0.0000000138
Coefficients
Predictor Estimate
Intercept -2.8791971289
OBS# -0.0001260984
AMOUNT 0.0000475598

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_TrainingScore

Data Mining: Logistic Regression – Prediction of Training Data Date: 13-Dec-2021 05:26:06

Output Navigator Elapsed Times in Milliseconds
Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total
PMML Model Training: Classification Summary Validation: Classification Summary 91 96 94 281
Training: Classification Summary
Confusion Matrix
Actual\Predicted 0 1
0 565 0
1 35 0
Error Report
Class # Cases # Errors % Error
0 565 0 0
1 35 35 100
Overall 600 35 5.8333333333
Metrics
Metric Value
Accuracy (#correct) 565
Accuracy (%correct) 94.1666666667
Specificity 1
Sensitivity (Recall) 0
Precision N/A
F1 score N/A
Success Class 1
Success Probability 0.5

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_ValidationScore

Data Mining: Logistic Regression – Prediction of Validation Data Date: 13-Dec-2021 05:26:06

Output Navigator Elapsed Times in Milliseconds
Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total
PMML Model Training: Classification Summary Validation: Classification Summary 91 96 94 281
Validation: Classification Summary
Confusion Matrix
Actual\Predicted 0 1
0 385 0
1 15 0
Error Report
Class # Cases # Errors % Error
0 385 0 0
1 15 15 100
Overall 400 15 3.75
Metrics
Metric Value
Accuracy (#correct) 385
Accuracy (%correct) 96.25
Specificity 1
Sensitivity (Recall) 0
Precision N/A
F1 score N/A
Success Class 1
Success Probability 0.5

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

LogReg_Stored

Data Mining: Logistic Regression – Stored Model Date: 13-Dec-2021 05:26:06

Output Navigator Elapsed Times in Milliseconds
Inputs Regression Summary Predictor Screening Coefficients Data Reading Time Algorithm Time Report Time Total
PMML Model Training: Classification Summary Validation: Classification Summary 91 96 94 281
PMML Model



2021-12-13 5:26:6

Inputs

Regression Summary

Predictor Screening

Coefficients

PMML Model

Training: Classification Summary

Validation: Classification Summary

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