Data mining Xlminer

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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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

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

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
<?xml version="1.0" encoding="utf-8"?>
<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
<Header copyright="Copyright (c) 2021 Frontline Systems Inc." description="NearestNeighborModel">
<Application name="XLMinerSDK" version="21.5.3.0"/>
<Timestamp>2021-12-13 5:34:54</Timestamp>
<ModelName/>
</Header>
<DataDictionary numberOfFields="32">
<DataField optype="continuous" dataType="double" name="OBS#"/>
<DataField optype="continuous" dataType="double" name="CHK_ACCT"/>
<DataField optype="continuous" dataType="double" name="DURATION"/>
<DataField optype="continuous" dataType="double" name="HISTORY"/>
<DataField optype="continuous" dataType="double" name="USED_CAR"/>
<DataField optype="continuous" dataType="double" name="FURNITURE"/>
<DataField optype="continuous" dataType="double" name="RADIO/TV"/>
<DataField optype="continuous" dataType="double" name="EDUCATION"/>
<DataField optype="continuous" dataType="double" name="RETRAINING"/>
<DataField optype="continuous" dataType="double" name="AMOUNT"/>
<DataField optype="continuous" dataType="double" name="SAV_ACCT"/>
<DataField optype="continuous" dataType="double" name="EMPLOYMENT"/>
<DataField optype="continuous" dataType="double" name="INSTALL_RATE"/>
<DataField optype="continuous" dataType="double" name="MALE_DIV"/>
<DataField optype="continuous" dataType="double" name="MALE_SINGLE"/>
<DataField optype="continuous" dataType="double" name="MALE_MAR_or_WID"/>
<DataField optype="continuous" dataType="double" name="CO-APPLICANT"/>
<DataField optype="continuous" dataType="double" name="GUARANTOR"/>
<DataField optype="continuous" dataType="double" name="PRESENT_RESIDENT"/>
<DataField optype="continuous" dataType="double" name="REAL_ESTATE"/>
<DataField optype="continuous" dataType="double" name="PROP_UNKN_NONE"/>
<DataField optype="continuous" dataType="double" name="AGE"/>
<DataField optype="continuous" dataType="double" name="OTHER_INSTALL"/>
<DataField optype="continuous" dataType="double" name="RENT"/>
<DataField optype="continuous" dataType="double" name="OWN_RES"/>
<DataField optype="continuous" dataType="double" name="NUM_CREDITS"/>
<DataField optype="continuous" dataType="double" name="JOB"/>
<DataField optype="continuous" dataType="double" name="NUM_DEPENDENTS"/>
<DataField optype="continuous" dataType="double" name="TELEPHONE"/>
<DataField optype="continuous" dataType="double" name="FOREIGN"/>
<DataField optype="continuous" dataType="double" name="RESPONSE"/>
<DataField optype="continuous" dataType="double" name="NEW_CAR"/>
</DataDictionary>
<NearestNeighborModel modelName="NearestNeighborModel" functionName="regression" algorithmName="NearestNeighbor" numberOfNeighbors="1" includeTies="1" stable="1" continuousScoringMethod="weightedAverage">
<MiningSchema>
<MiningField name="NEW_CAR" usageType="predicted"/>
<MiningField name="OBS#" usageType="active"/>
<MiningField name="CHK_ACCT" usageType="active"/>
<MiningField name="DURATION" usageType="active"/>
<MiningField name="HISTORY" usageType="active"/>
<MiningField name="USED_CAR" usageType="active"/>
<MiningField name="FURNITURE" usageType="active"/>
<MiningField name="RADIO/TV" usageType="active"/>
<MiningField name="EDUCATION" usageType="active"/>
<MiningField name="RETRAINING" usageType="active"/>
<MiningField name="AMOUNT" usageType="active"/>
<MiningField name="SAV_ACCT" usageType="active"/>
<MiningField name="EMPLOYMENT" usageType="active"/>
<MiningField name="INSTALL_RATE" usageType="active"/>
<MiningField name="MALE_DIV" usageType="active"/>
<MiningField name="MALE_SINGLE" usageType="active"/>
<MiningField name="MALE_MAR_or_WID" usageType="active"/>
<MiningField name="CO-APPLICANT" usageType="active"/>
<MiningField name="GUARANTOR" usageType="active"/>
<MiningField name="PRESENT_RESIDENT" usageType="active"/>
<MiningField name="REAL_ESTATE" usageType="active"/>
<MiningField name="PROP_UNKN_NONE" usageType="active"/>
<MiningField name="AGE" usageType="active"/>
<MiningField name="OTHER_INSTALL" usageType="active"/>
<MiningField name="RENT" usageType="active"/>
<MiningField name="OWN_RES" usageType="active"/>
<MiningField name="NUM_CREDITS" usageType="active"/>
<MiningField name="JOB" usageType="active"/>
<MiningField name="NUM_DEPENDENTS" usageType="active"/>
<MiningField name="TELEPHONE" usageType="active"/>
<MiningField name="FOREIGN" usageType="active"/>
<MiningField name="RESPONSE" usageType="active"/>
</MiningSchema>
<Output>
<OutputField optype="continuous" dataType="double" name="Predicted_NEW_CAR" feature="predictedValue"/>
</Output>
<LocalTransformations/>
<TrainingInstances recordCount="1000" fieldCount="32" isTransformed="false">
<InstanceFields>
<InstanceField field="OBS#" column="col0"/>
<InstanceField field="CHK_ACCT" column="col1"/>
<InstanceField field="DURATION" column="col2"/>
<InstanceField field="HISTORY" column="col3"/>
<InstanceField field="USED_CAR" column="col4"/>
<InstanceField field="FURNITURE" column="col5"/>
<InstanceField field="RADIO/TV" column="col6"/>
<InstanceField field="EDUCATION" column="col7"/>
<InstanceField field="RETRAINING" column="col8"/>
<InstanceField field="AMOUNT" column="col9"/>
<InstanceField field="SAV_ACCT" column="col10"/>
<InstanceField field="EMPLOYMENT" column="col11"/>
<InstanceField field="INSTALL_RATE" column="col12"/>
<InstanceField field="MALE_DIV" column="col13"/>
<InstanceField field="MALE_SINGLE" column="col14"/>
<InstanceField field="MALE_MAR_or_WID" column="col15"/>
<InstanceField field="CO-APPLICANT" column="col16"/>
<InstanceField field="GUARANTOR" column="col17"/>
<InstanceField field="PRESENT_RESIDENT" column="col18"/>
<InstanceField field="REAL_ESTATE" column="col19"/>
<InstanceField field="PROP_UNKN_NONE" column="col20"/>
<InstanceField field="AGE" column="col21"/>
<InstanceField field="OTHER_INSTALL" column="col22"/>
<InstanceField field="RENT" column="col23"/>
<InstanceField field="OWN_RES" column="col24"/>
<InstanceField field="NUM_CREDITS" column="col25"/>
<InstanceField field="JOB" column="col26"/>
<InstanceField field="NUM_DEPENDENTS" column="col27"/>
<InstanceField field="TELEPHONE" column="col28"/>
<InstanceField field="FOREIGN" column="col29"/>
<InstanceField field="RESPONSE" column="col30"/>
<InstanceField field="NEW_CAR" column="col31"/>
</InstanceFields>
</TrainingInstances>
<InlineTable>
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<row>
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</row>
</InlineTable>
<ComparisonMeasure kind="distance">
<squaredEuclidean/>
</ComparisonMeasure>
<KNNInputs>
<KNNInput field="OBS#" compareFunction="absDiff"/>
<KNNInput field="CHK_ACCT" compareFunction="absDiff"/>
<KNNInput field="DURATION" compareFunction="absDiff"/>
<KNNInput field="HISTORY" compareFunction="absDiff"/>
<KNNInput field="USED_CAR" compareFunction="absDiff"/>
<KNNInput field="FURNITURE" compareFunction="absDiff"/>
<KNNInput field="RADIO/TV" compareFunction="absDiff"/>
<KNNInput field="EDUCATION" compareFunction="absDiff"/>
<KNNInput field="RETRAINING" compareFunction="absDiff"/>
<KNNInput field="AMOUNT" compareFunction="absDiff"/>
<KNNInput field="SAV_ACCT" compareFunction="absDiff"/>
<KNNInput field="EMPLOYMENT" compareFunction="absDiff"/>
<KNNInput field="INSTALL_RATE" compareFunction="absDiff"/>
<KNNInput field="MALE_DIV" compareFunction="absDiff"/>
<KNNInput field="MALE_SINGLE" compareFunction="absDiff"/>
<KNNInput field="MALE_MAR_or_WID" compareFunction="absDiff"/>
<KNNInput field="CO-APPLICANT" compareFunction="absDiff"/>
<KNNInput field="GUARANTOR" compareFunction="absDiff"/>
<KNNInput field="PRESENT_RESIDENT" compareFunction="absDiff"/>
<KNNInput field="REAL_ESTATE" compareFunction="absDiff"/>
<KNNInput field="PROP_UNKN_NONE" compareFunction="absDiff"/>
<KNNInput field="AGE" compareFunction="absDiff"/>
<KNNInput field="OTHER_INSTALL" compareFunction="absDiff"/>
<KNNInput field="RENT" compareFunction="absDiff"/>
<KNNInput field="OWN_RES" compareFunction="absDiff"/>
<KNNInput field="NUM_CREDITS" compareFunction="absDiff"/>
<KNNInput field="JOB" compareFunction="absDiff"/>
<KNNInput field="NUM_DEPENDENTS" compareFunction="absDiff"/>
<KNNInput field="TELEPHONE" compareFunction="absDiff"/>
<KNNInput field="FOREIGN" compareFunction="absDiff"/>
<KNNInput field="RESPONSE" compareFunction="absDiff"/>
</KNNInputs>
</NearestNeighborModel>
</PMML>

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
Record 78 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
Record 80 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
Record 81 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
Record 82 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
Record 83 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
Record 84 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
Record 88 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
Record 89 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
Record 91 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
Record 92 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
Record 95 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
Record 97 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
Record 98 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
Record 100 100 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
Record 106 106 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
Record 107 107 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
Record 108 108 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
Record 109 109 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
Record 110 110 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
Record 112 112 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
Record 113 113 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
Record 114 114 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
Record 115 115 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
Record 118 118 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
Record 119 119 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
Record 120 120 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
Record 122 122 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
Record 123 123 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
Record 128 128 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
Record 129 129 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
Record 130 130 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
Record 132 132 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
Record 133 133 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
Record 134 134 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
Record 136 136 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
Record 141 141 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
Record 142 142 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
Record 143 143 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
Record 145 145 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
Record 146 146 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
Record 148 148 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
Record 150 150 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
Record 153 153 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
Record 156 156 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
Record 157 157 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
Record 159 159 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
Record 161 161 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
Record 164 164 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
Record 166 166 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
Record 167 167 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
Record 168 168 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
Record 169 169 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
Record 171 171 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
Record 174 174 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
Record 179 179 3 12 2 0 0 0 1 0 0 1963 0 3 4 0 1 0 0 0 2 0 0 31 0 1 0 2 3 2 1 0 1
Record 181 181 3 36 3 0 0 0 0 0 1 9572 0 1 1 1 0 0 0 0 1 0 0 28 0 0 1 2 2 1 0 0 0
Record 182 182 1 36 3 0 0 0 0 0 1 4455 0 2 2 1 0 0 0 0 2 1 0 30 1 0 1 2 3 1 1 0 0
Record 185 185 1 18 4 1 0 0 0 0 0 884 0 4 4 0 1 0 0 0 4 0 0 36 1 0 1 1 2 2 1 0 0
Record 186 186 3 15 4 0 0 0 1 0 0 1360 0 2 4 0 1 0 0 0 2 0 0 31 0 0 1 2 2 1 0 0 1
Record 187 187 1 9 1 0 1 0 0 0 0 5129 0 4 2 0 0 0 0 0 4 0 1 74 1 0 0 1 3 2 1 0 0
Record 188 188 1 16 4 1 0 0 0 0 0 1175 0 0 2 0 1 0 0 0 3 0 0 68 0 0 0 3 0 1 1 0 1
Record 189 189 0 12 2 0 0 0 1 0 0 674 1 3 4 0 0 1 0 0 1 0 0 20 0 0 1 1 2 1 0 0 0
Record 190 190 1 18 0 0 0 1 0 0 0 3244 0 2 1 0 0 0 0 0 4 0 0 33 1 0 1 2 2 1 1 0 1
Record 192 192 1 48 0 0 0 0 0 0 1 3844 1 3 4 0 1 0 0 0 4 0 1 34 0 0 0 1 1 2 0 0 0
Record 193 193 1 27 2 0 0 0 0 0 1 3915 0 2 4 0 1 0 0 0 2 0 0 36 0 0 1 1 2 2 1 0 0
Record 195 195 1 45 2 0 0 0 1 0 0 3031 1 2 4 0 1 0 0 1 4 0 0 21 0 1 0 1 2 1 0 0 0
Record 196 196 1 9 4 0 0 0 0 1 0 1501 0 4 2 0 0 0 0 0 3 0 0 34 0 0 1 2 3 1 1 0 0
Record 200 200 1 18 3 0 0 1 0 0 0 4297 0 4 4 1 0 0 0 0 3 0 1 40 0 0 1 1 3 1 1 0 0
Record 201 201 3 9 4 0 0 0 0 1 0 936 2 4 4 0 1 0 0 0 2 0 0 52 0 0 1 2 2 1 1 0 1
Record 202 202 0 12 2 1 0 0 0 0 0 1168 0 2 4 0 0 1 0 0 3 1 0 27 0 0 1 1 1 1 0 0 1
Record 205 205 3 12 4 1 0 0 0 0 0 1495 0 4 4 0 1 0 0 0 1 1 0 38 0 0 1 2 1 2 0 0 1
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CT_Output1

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

CT_TrainingScore1

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

CT_ValidationScore1

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

CT_Stored1

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
<?xml version="1.0" encoding="utf-8"?>
<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
<Header copyright="Copyright (c) 2021 Frontline Systems Inc." description="TreeModel">
<Application name="XLMinerSDK" version="21.5.3.0"/>
<Timestamp>2021-12-13 5:58:14</Timestamp>
<ModelName/>
</Header>
<DataDictionary numberOfFields="3">
<DataField optype="continuous" dataType="double" name="OBS#"/>
<DataField optype="continuous" dataType="double" name="AMOUNT"/>
<DataField optype="categorical" dataType="string" name="EDUCATION">
<Value value="0"/>
<Value value="1"/>
</DataField>
</DataDictionary>
<TreeModel modelName="TreeModel" functionName="classification" algorithmName="DecisionTree" splitCharacteristic="binarySplit" missingValueStrategy="defaultChild" noTrueChildStrategy="returnLastPrediction">
<LocalTransformations/>
<MiningSchema>
<MiningField name="EDUCATION" usageType="predicted"/>
<MiningField name="OBS#" usageType="active"/>
<MiningField name="AMOUNT" usageType="active"/>
</MiningSchema>
<Output>
<OutputField optype="categorical" dataType="string" name="Predicted_EDUCATION" feature="predictedValue"/>
</Output>
<Targets>
<Target field="EDUCATION" optype="categorical">
<TargetValue value="0" priorProbability="0.94166666666666665"/>
<TargetValue value="1" priorProbability="0.058333333333333334"/>
</Target>
</Targets>
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<True/>
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<SimplePredicate field="OBS#" operator="greaterOrEqual" value="990.5"/>
<ScoreDistribution value="0" recordCount="6"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="35" score="1" recordCount="1">
<SimplePredicate field="OBS#" operator="lessThan" value="991.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
<Node id="36" score="0" recordCount="6">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="991.5"/>
<ScoreDistribution value="0" recordCount="6"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
</Node>
</Node>
</Node>
<Node id="2" score="0" recordCount="87" defaultChild="6">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="5976"/>
<ScoreDistribution value="0" recordCount="77"/>
<ScoreDistribution value="1" recordCount="10"/>
<Node id="5" score="0" recordCount="10" defaultChild="12">
<SimplePredicate field="AMOUNT" operator="lessThan" value="6288.5"/>
<ScoreDistribution value="0" recordCount="6"/>
<ScoreDistribution value="1" recordCount="4"/>
<Node id="11" score="0" recordCount="7" defaultChild="24">
<SimplePredicate field="OBS#" operator="lessThan" value="754.5"/>
<ScoreDistribution value="0" recordCount="6"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="23" score="1" recordCount="1">
<SimplePredicate field="OBS#" operator="lessThan" value="41"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
<Node id="24" score="0" recordCount="6">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="41"/>
<ScoreDistribution value="0" recordCount="6"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
<Node id="12" score="1" recordCount="3">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="754.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="3"/>
</Node>
</Node>
<Node id="6" score="0" recordCount="77" defaultChild="14">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="6288.5"/>
<ScoreDistribution value="0" recordCount="71"/>
<ScoreDistribution value="1" recordCount="6"/>
<Node id="13" score="0" recordCount="56" defaultChild="26">
<SimplePredicate field="OBS#" operator="lessThan" value="784.5"/>
<ScoreDistribution value="0" recordCount="50"/>
<ScoreDistribution value="1" recordCount="6"/>
<Node id="25" score="0" recordCount="55" defaultChild="38">
<SimplePredicate field="OBS#" operator="lessThan" value="768"/>
<ScoreDistribution value="0" recordCount="50"/>
<ScoreDistribution value="1" recordCount="5"/>
<Node id="37" score="0" recordCount="46" defaultChild="50">
<SimplePredicate field="AMOUNT" operator="lessThan" value="11675"/>
<ScoreDistribution value="0" recordCount="43"/>
<ScoreDistribution value="1" recordCount="3"/>
<Node id="49" score="0" recordCount="34" defaultChild="64">
<SimplePredicate field="AMOUNT" operator="lessThan" value="8529.5"/>
<ScoreDistribution value="0" recordCount="31"/>
<ScoreDistribution value="1" recordCount="3"/>
<Node id="63" score="0" recordCount="33" defaultChild="76">
<SimplePredicate field="AMOUNT" operator="lessThan" value="8302"/>
<ScoreDistribution value="0" recordCount="31"/>
<ScoreDistribution value="1" recordCount="2"/>
<Node id="75" score="0" recordCount="27" defaultChild="84">
<SimplePredicate field="OBS#" operator="lessThan" value="648.5"/>
<ScoreDistribution value="0" recordCount="26"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="83" score="0" recordCount="6" defaultChild="94">
<SimplePredicate field="AMOUNT" operator="lessThan" value="6917.5"/>
<ScoreDistribution value="0" recordCount="5"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="93" score="0" recordCount="5">
<SimplePredicate field="AMOUNT" operator="lessThan" value="6822.5"/>
<ScoreDistribution value="0" recordCount="5"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
<Node id="94" score="1" recordCount="1">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="6822.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
</Node>
<Node id="84" score="0" recordCount="21">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="6917.5"/>
<ScoreDistribution value="0" recordCount="21"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
<Node id="76" score="0" recordCount="6" defaultChild="86">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="648.5"/>
<ScoreDistribution value="0" recordCount="5"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="85" score="1" recordCount="1">
<SimplePredicate field="OBS#" operator="lessThan" value="658.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
<Node id="86" score="0" recordCount="5">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="658.5"/>
<ScoreDistribution value="0" recordCount="5"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
</Node>
<Node id="64" score="1" recordCount="1">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="8302"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
</Node>
<Node id="50" score="0" recordCount="12">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="8529.5"/>
<ScoreDistribution value="0" recordCount="12"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
<Node id="38" score="0" recordCount="9" defaultChild="52">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="11675"/>
<ScoreDistribution value="0" recordCount="7"/>
<ScoreDistribution value="1" recordCount="2"/>
<Node id="51" score="1" recordCount="1">
<SimplePredicate field="OBS#" operator="lessThan" value="97"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
<Node id="52" score="0" recordCount="8" defaultChild="66">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="97"/>
<ScoreDistribution value="0" recordCount="7"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="65" score="1" recordCount="1">
<SimplePredicate field="AMOUNT" operator="lessThan" value="11849"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
<Node id="66" score="0" recordCount="7">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="11849"/>
<ScoreDistribution value="0" recordCount="7"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
</Node>
</Node>
<Node id="26" score="1" recordCount="1">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="768"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
</Node>
<Node id="14" score="0" recordCount="21">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="784.5"/>
<ScoreDistribution value="0" recordCount="21"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
</Node>
</Node>
</TreeModel>
</PMML>

LogReg_Output1

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

LogReg_TrainingScore1

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

LogReg_ValidationScore1

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

LogReg_Stored1

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
<?xml version="1.0" encoding="utf-8"?>
<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
<Header copyright="Copyright (c) 2021 Frontline Systems Inc." description="RegressionModel">
<Application name="XLMinerSDK" version="21.5.3.0"/>
<Timestamp>2021-12-13 5:49:29</Timestamp>
<ModelName/>
</Header>
<DataDictionary numberOfFields="3">
<DataField optype="continuous" dataType="double" name="OBS#"/>
<DataField optype="continuous" dataType="double" name="AMOUNT"/>
<DataField optype="categorical" dataType="string" name="EDUCATION">
<Value value="0"/>
<Value value="1"/>
</DataField>
</DataDictionary>
<RegressionModel modelName="RegressionModel" functionName="classification" algorithmName="LogisticRegression">
<MiningSchema>
<MiningField name="EDUCATION" usageType="predicted"/>
<MiningField name="OBS#" usageType="active"/>
<MiningField name="AMOUNT" usageType="active"/>
</MiningSchema>
<Output>
<OutputField optype="categorical" dataType="string" name="Predicted_EDUCATION" feature="predictedValue"/>
</Output>
<Targets>
<Target field="EDUCATION" optype="categorical" successClass="1">
<TargetValue value="0" priorProbability="0.94166666666666665"/>
<TargetValue value="1" priorProbability="0.058333333333333334"/>
</Target>
</Targets>
<LocalTransformations/>
<RegressionTable intercept="-2.8791971289443374">
<NumericPredictor name="OBS#" exponent="1" coefficient="-0.00012609835612062059"/>
<NumericPredictor name="AMOUNT" exponent="1" coefficient="4.7559782934717943e-05"/>
</RegressionTable>
</RegressionModel>
</PMML>

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

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

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

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
<?xml version="1.0" encoding="utf-8"?>
<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
<Header copyright="Copyright (c) 2021 Frontline Systems Inc." description="TreeModel">
<Application name="XLMinerSDK" version="21.5.3.0"/>
<Timestamp>2021-12-13 5:47:52</Timestamp>
<ModelName/>
</Header>
<DataDictionary numberOfFields="3">
<DataField optype="continuous" dataType="double" name="OBS#"/>
<DataField optype="continuous" dataType="double" name="AMOUNT"/>
<DataField optype="categorical" dataType="string" name="EDUCATION">
<Value value="0"/>
<Value value="1"/>
</DataField>
</DataDictionary>
<TreeModel modelName="TreeModel" functionName="classification" algorithmName="DecisionTree" splitCharacteristic="binarySplit" missingValueStrategy="defaultChild" noTrueChildStrategy="returnLastPrediction">
<LocalTransformations/>
<MiningSchema>
<MiningField name="EDUCATION" usageType="predicted"/>
<MiningField name="OBS#" usageType="active"/>
<MiningField name="AMOUNT" usageType="active"/>
</MiningSchema>
<Output>
<OutputField optype="categorical" dataType="string" name="Predicted_EDUCATION" feature="predictedValue"/>
</Output>
<Targets>
<Target field="EDUCATION" optype="categorical">
<TargetValue value="0" priorProbability="0.94166666666666665"/>
<TargetValue value="1" priorProbability="0.058333333333333334"/>
</Target>
</Targets>
<Node id="0" score="0" recordCount="600" defaultChild="2">
<True/>
<ScoreDistribution value="0" recordCount="565"/>
<ScoreDistribution value="1" recordCount="35"/>
<Node id="1" score="0" recordCount="513" defaultChild="4">
<SimplePredicate field="AMOUNT" operator="lessThan" value="5976"/>
<ScoreDistribution value="0" recordCount="488"/>
<ScoreDistribution value="1" recordCount="25"/>
<Node id="3" score="0" recordCount="38" defaultChild="8">
<SimplePredicate field="AMOUNT" operator="lessThan" value="752"/>
<ScoreDistribution value="0" recordCount="33"/>
<ScoreDistribution value="1" recordCount="5"/>
<Node id="7" score="0" recordCount="32" defaultChild="16">
<SimplePredicate field="AMOUNT" operator="lessThan" value="714"/>
<ScoreDistribution value="0" recordCount="30"/>
<ScoreDistribution value="1" recordCount="2"/>
<Node id="15" score="0" recordCount="10" defaultChild="28">
<SimplePredicate field="AMOUNT" operator="lessThan" value="451"/>
<ScoreDistribution value="0" recordCount="8"/>
<ScoreDistribution value="1" recordCount="2"/>
<Node id="27" score="0" recordCount="9" defaultChild="40">
<SimplePredicate field="AMOUNT" operator="lessThan" value="440.5"/>
<ScoreDistribution value="0" recordCount="8"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="39" score="1" recordCount="2" defaultChild="54">
<SimplePredicate field="OBS#" operator="lessThan" value="135"/>
<ScoreDistribution value="0" recordCount="1"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="53" score="0" recordCount="1">
<SimplePredicate field="OBS#" operator="lessThan" value="70"/>
<ScoreDistribution value="0" recordCount="1"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
<Node id="54" score="1" recordCount="1">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="70"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
</Node>
<Node id="40" score="0" recordCount="7">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="135"/>
<ScoreDistribution value="0" recordCount="7"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
<Node id="28" score="1" recordCount="1">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="440.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
</Node>
<Node id="16" score="0" recordCount="22">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="451"/>
<ScoreDistribution value="0" recordCount="22"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
<Node id="8" score="1" recordCount="6" defaultChild="18">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="714"/>
<ScoreDistribution value="0" recordCount="3"/>
<ScoreDistribution value="1" recordCount="3"/>
<Node id="17" score="1" recordCount="1">
<SimplePredicate field="AMOUNT" operator="lessThan" value="723"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
<Node id="18" score="0" recordCount="5" defaultChild="30">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="723"/>
<ScoreDistribution value="0" recordCount="3"/>
<ScoreDistribution value="1" recordCount="2"/>
<Node id="29" score="0" recordCount="4" defaultChild="42">
<SimplePredicate field="AMOUNT" operator="lessThan" value="747.5"/>
<ScoreDistribution value="0" recordCount="3"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="41" score="1" recordCount="2" defaultChild="56">
<SimplePredicate field="AMOUNT" operator="lessThan" value="734"/>
<ScoreDistribution value="0" recordCount="1"/>
<ScoreDistribution value="1" recordCount="1"/>
<Node id="55" score="0" recordCount="1">
<SimplePredicate field="OBS#" operator="lessThan" value="331.5"/>
<ScoreDistribution value="0" recordCount="1"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
<Node id="56" score="1" recordCount="1">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="331.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
</Node>
<Node id="42" score="0" recordCount="2">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="734"/>
<ScoreDistribution value="0" recordCount="2"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
</Node>
<Node id="30" score="1" recordCount="1">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="747.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
</Node>
</Node>
</Node>
</Node>
<Node id="4" score="0" recordCount="475" defaultChild="10">
<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="752"/>
<ScoreDistribution value="0" recordCount="455"/>
<ScoreDistribution value="1" recordCount="20"/>
<Node id="9" score="0" recordCount="342" defaultChild="20">
<SimplePredicate field="OBS#" operator="lessThan" value="712.5"/>
<ScoreDistribution value="0" recordCount="324"/>
<ScoreDistribution value="1" recordCount="18"/>
<Node id="19" score="0" recordCount="341" defaultChild="32">
<SimplePredicate field="OBS#" operator="lessThan" value="708.5"/>
<ScoreDistribution value="0" recordCount="324"/>
<ScoreDistribution value="1" recordCount="17"/>
<Node id="31" score="0" recordCount="253" defaultChild="44">
<SimplePredicate field="OBS#" operator="lessThan" value="526"/>
<ScoreDistribution value="0" recordCount="246"/>
<ScoreDistribution value="1" recordCount="7"/>
<Node id="43" score="0" recordCount="127" defaultChild="58">
<SimplePredicate field="AMOUNT" operator="lessThan" value="2035"/>
<ScoreDistribution value="0" recordCount="121"/>
<ScoreDistribution value="1" recordCount="6"/>
<Node id="57" score="0" recordCount="126" defaultChild="68">
<SimplePredicate field="AMOUNT" operator="lessThan" value="1998"/>
<ScoreDistribution value="0" recordCount="121"/>
<ScoreDistribution value="1" recordCount="5"/>
<Node id="67" score="0" recordCount="41">
<SimplePredicate field="OBS#" operator="lessThan" value="192"/>
<ScoreDistribution value="0" recordCount="41"/>
<ScoreDistribution value="1" recordCount="0"/>
</Node>
<Node id="68" score="0" recordCount="85" defaultChild="78">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="192"/>
<ScoreDistribution value="0" recordCount="80"/>
<ScoreDistribution value="1" recordCount="5"/>
<Node id="77" score="1" recordCount="2">
<SimplePredicate field="OBS#" operator="lessThan" value="201.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="2"/>
</Node>
<Node id="78" score="0" recordCount="83" defaultChild="88">
<SimplePredicate field="OBS#" operator="greaterOrEqual" value="201.5"/>
<ScoreDistribution value="0" recordCount="80"/>
<ScoreDistribution value="1" recordCount="3"/>
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<SimplePredicate field="OBS#" operator="greaterOrEqual" value="532.5"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="536.5"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="535.5"/>
<ScoreDistribution value="0" recordCount="2"/>
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<SimplePredicate field="OBS#" operator="greaterOrEqual" value="535.5"/>
<ScoreDistribution value="0" recordCount="0"/>
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<SimplePredicate field="AMOUNT" operator="lessThan" value="1953"/>
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<ScoreDistribution value="1" recordCount="5"/>
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<SimplePredicate field="OBS#" operator="greaterOrEqual" value="708.5"/>
<ScoreDistribution value="0" recordCount="0"/>
<ScoreDistribution value="1" recordCount="1"/>
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</Node>
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<SimplePredicate field="OBS#" operator="greaterOrEqual" value="712.5"/>
<ScoreDistribution value="0" recordCount="131"/>
<ScoreDistribution value="1" recordCount="2"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="990.5"/>
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<SimplePredicate field="AMOUNT" operator="lessThan" value="3683.5"/>
<ScoreDistribution value="0" recordCount="106"/>
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<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="3683.5"/>
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<ScoreDistribution value="1" recordCount="1"/>
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<SimplePredicate field="AMOUNT" operator="lessThan" value="3737"/>
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<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="3737"/>
<ScoreDistribution value="0" recordCount="19"/>
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</Node>
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<SimplePredicate field="OBS#" operator="greaterOrEqual" value="990.5"/>
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<ScoreDistribution value="1" recordCount="1"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="991.5"/>
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<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="5976"/>
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<ScoreDistribution value="1" recordCount="10"/>
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<SimplePredicate field="AMOUNT" operator="lessThan" value="6288.5"/>
<ScoreDistribution value="0" recordCount="6"/>
<ScoreDistribution value="1" recordCount="4"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="754.5"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="41"/>
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<ScoreDistribution value="1" recordCount="1"/>
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<SimplePredicate field="OBS#" operator="greaterOrEqual" value="41"/>
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</Node>
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<SimplePredicate field="AMOUNT" operator="greaterOrEqual" value="6288.5"/>
<ScoreDistribution value="0" recordCount="71"/>
<ScoreDistribution value="1" recordCount="6"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="784.5"/>
<ScoreDistribution value="0" recordCount="50"/>
<ScoreDistribution value="1" recordCount="6"/>
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<SimplePredicate field="OBS#" operator="lessThan" value="768"/>
<ScoreDistribution value="0" recordCount="50"/>
<ScoreDistribution value="1" recordCount="5"/>
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<SimplePredicate field="AMOUNT" operator="lessThan" value="11675"/>
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<ScoreDistribution value="1" recordCount="3"/>
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<ScoreDistribution value="1" recordCount="2"/>
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<ScoreDistribution value="1" recordCount="1"/>
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</Node>
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<ScoreDistribution value="1" recordCount="1"/>
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</Node>
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</TreeModel>
</PMML>

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

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

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

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
<?xml version="1.0" encoding="utf-8"?>
<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
<Header copyright="Copyright (c) 2021 Frontline Systems Inc." description="RegressionModel">
<Application name="XLMinerSDK" version="21.5.3.0"/>
<Timestamp>2021-12-13 5:26:6</Timestamp>
<ModelName/>
</Header>
<DataDictionary numberOfFields="3">
<DataField optype="continuous" dataType="double" name="OBS#"/>
<DataField optype="continuous" dataType="double" name="AMOUNT"/>
<DataField optype="categorical" dataType="string" name="EDUCATION">
<Value value="0"/>
<Value value="1"/>
</DataField>
</DataDictionary>
<RegressionModel modelName="RegressionModel" functionName="classification" algorithmName="LogisticRegression">
<MiningSchema>
<MiningField name="EDUCATION" usageType="predicted"/>
<MiningField name="OBS#" usageType="active"/>
<MiningField name="AMOUNT" usageType="active"/>
</MiningSchema>
<Output>
<OutputField optype="categorical" dataType="string" name="Predicted_EDUCATION" feature="predictedValue"/>
</Output>
<Targets>
<Target field="EDUCATION" optype="categorical" successClass="1">
<TargetValue value="0" priorProbability="0.94166666666666665"/>
<TargetValue value="1" priorProbability="0.058333333333333334"/>
</Target>
</Targets>
<LocalTransformations/>
<RegressionTable intercept="-2.8791971289443374">
<NumericPredictor name="OBS#" exponent="1" coefficient="-0.00012609835612062059"/>
<NumericPredictor name="AMOUNT" exponent="1" coefficient="4.7559782934717943e-05"/>
</RegressionTable>
</RegressionModel>
</PMML>