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credit-g

credit-g

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Author: Source: Unknown - Please cite: Description of the German credit dataset. 1. Title: German Credit data 2. Source Information Professor Dr. Hans Hofmann Institut f"ur Statistik und "Okonometrie Universit"at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13 3. Number of Instances: 1000 Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical/symbolic attributes and is in the file "german.data". For algorithms that need numerical attributes, Strathclyde University produced the file "german.data-numeric". This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This was the form used by StatLog. 6. Number of Attributes german: 20 (7 numerical, 13 categorical) Number of Attributes german.numer: 24 (24 numerical) 7. Attribute description for german Attribute 1: (qualitative) Status of existing checking account A11 : ... < 0 DM A12 : 0 <= ... < 200 DM A13 : ... >= 200 DM / salary assignments for at least 1 year A14 : no checking account Attribute 2: (numerical) Duration in month Attribute 3: (qualitative) Credit history A30 : no credits taken/ all credits paid back duly A31 : all credits at this bank paid back duly A32 : existing credits paid back duly till now A33 : delay in paying off in the past A34 : critical account/ other credits existing (not at this bank) Attribute 4: (qualitative) Purpose A40 : car (new) A41 : car (used) A42 : furniture/equipment A43 : radio/television A44 : domestic appliances A45 : repairs A46 : education A47 : (vacation - does not exist?) A48 : retraining A49 : business A410 : others Attribute 5: (numerical) Credit amount Attibute 6: (qualitative) Savings account/bonds A61 : ... < 100 DM A62 : 100 <= ... < 500 DM A63 : 500 <= ... < 1000 DM A64 : .. >= 1000 DM A65 : unknown/ no savings account Attribute 7: (qualitative) Present employment since A71 : unemployed A72 : ... < 1 year A73 : 1 <= ... < 4 years A74 : 4 <= ... < 7 years A75 : .. >= 7 years Attribute 8: (numerical) Installment rate in percentage of disposable income Attribute 9: (qualitative) Personal status and sex A91 : male : divorced/separated A92 : female : divorced/separated/married A93 : male : single A94 : male : married/widowed A95 : female : single Attribute 10: (qualitative) Other debtors / guarantors A101 : none A102 : co-applicant A103 : guarantor Attribute 11: (numerical) Present residence since Attribute 12: (qualitative) Property A121 : real estate A122 : if not A121 : building society savings agreement/ life insurance A123 : if not A121/A122 : car or other, not in attribute 6 A124 : unknown / no property Attribute 13: (numerical) Age in years Attribute 14: (qualitative) Other installment plans A141 : bank A142 : stores A143 : none Attribute 15: (qualitative) Housing A151 : rent A152 : own A153 : for free Attribute 16: (numerical) Number of existing credits at this bank Attribute 17: (qualitative) Job A171 : unemployed/ unskilled - non-resident A172 : unskilled - resident A173 : skilled employee / official A174 : management/ self-employed/ highly qualified employee/ officer Attribute 18: (numerical) Number of people being liable to provide maintenance for Attribute 19: (qualitative) Telephone A191 : none A192 : yes, registered under the customers name Attribute 20: (qualitative) foreign worker A201 : yes A202 : no 8. Cost Matrix This dataset requires use of a cost matrix (see below) 1 2 ---------------------------- 1 0 1 ----------------------- 2 5 0 (1 = Good, 2 = Bad) the rows represent the actual classification and the columns the predicted classification. It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1). Relabeled values in attribute checking_status From: A11 To: '<0' From: A12 To: '0<=X<200' From: A13 To: '>=200' From: A14 To: 'no checking' Relabeled values in attribute credit_history From: A30 To: 'no credits/all paid' From: A31 To: 'all paid' From: A32 To: 'existing paid' From: A33 To: 'delayed previously' From: A34 To: 'critical/other existing credit' Relabeled values in attribute purpose From: A40 To: 'new car' From: A41 To: 'used car' From: A42 To: furniture/equipment From: A43 To: radio/tv From: A44 To: 'domestic appliance' From: A45 To: repairs From: A46 To: education From: A47 To: vacation From: A48 To: retraining From: A49 To: business From: A410 To: other Relabeled values in attribute savings_status From: A61 To: '<100' From: A62 To: '100<=X<500' From: A63 To: '500<=X<1000' From: A64 To: '>=1000' From: A65 To: 'no known savings' Relabeled values in attribute employment From: A71 To: unemployed From: A72 To: '<1' From: A73 To: '1<=X<4' From: A74 To: '4<=X<7' From: A75 To: '>=7' Relabeled values in attribute personal_status From: A91 To: 'male div/sep' From: A92 To: 'female div/dep/mar' From: A93 To: 'male single' From: A94 To: 'male mar/wid' From: A95 To: 'female single' Relabeled values in attribute other_parties From: A101 To: none From: A102 To: 'co applicant' From: A103 To: guarantor Relabeled values in attribute property_magnitude From: A121 To: 'real estate' From: A122 To: 'life insurance' From: A123 To: car From: A124 To: 'no known property' Relabeled values in attribute other_payment_plans From: A141 To: bank From: A142 To: stores From: A143 To: none Relabeled values in attribute housing From: A151 To: rent From: A152 To: own From: A153 To: 'for free' Relabeled values in attribute job From: A171 To: 'unemp/unskilled non res' From: A172 To: 'unskilled resident' From: A173 To: skilled From: A174 To: 'high qualif/self emp/mgmt' Relabeled values in attribute own_telephone From: A191 To: none From: A192 To: yes Relabeled values in attribute foreign_worker From: A201 To: yes From: A202 To: no Relabeled values in attribute class From: 1 To: good From: 2 To: bad

21 features

class (target)nominal2 unique values
0 missing
checking_statusnominal4 unique values
0 missing
durationnumeric33 unique values
0 missing
credit_historynominal5 unique values
0 missing
purposenominal10 unique values
0 missing
credit_amountnumeric921 unique values
0 missing
savings_statusnominal5 unique values
0 missing
employmentnominal5 unique values
0 missing
installment_commitmentnumeric4 unique values
0 missing
personal_statusnominal4 unique values
0 missing
other_partiesnominal3 unique values
0 missing
residence_sincenumeric4 unique values
0 missing
property_magnitudenominal4 unique values
0 missing
agenumeric53 unique values
0 missing
other_payment_plansnominal3 unique values
0 missing
housingnominal3 unique values
0 missing
existing_creditsnumeric4 unique values
0 missing
jobnominal4 unique values
0 missing
num_dependentsnumeric2 unique values
0 missing
own_telephonenominal2 unique values
0 missing
foreign_workernominal2 unique values
0 missing

107 properties

1000
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
14
Number of nominal attributes.
0.88
Entropy of the target attribute values.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
700
Number of instances belonging to the most frequent class.
0.23
Minimal entropy among attributes.
0.92
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.67
Maximum entropy among attributes.
-1.38
Minimum kurtosis among attributes of the numeric type.
2.97
Second quartile (Median) of means among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4.29
Maximum kurtosis among attributes of the numeric type.
1.16
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
3271.26
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1.09
Second quartile (Median) of skewness among attributes of the numeric type.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
0.09
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
14.29
Percentage of binary attributes.
1.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
43.59
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
10
The maximum number of distinct values among attributes of the nominal type.
-0.53
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.87
Third quartile of entropy among attributes.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.95
Maximum skewness among attributes of the numeric type.
0.36
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1.65
Third quartile of kurtosis among attributes of the numeric type.
0.57
Average class difference between consecutive instances.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2822.74
Maximum standard deviation of attributes of the numeric type.
30
Percentage of instances belonging to the least frequent class.
33.33
Percentage of numeric attributes.
35.55
Third quartile of means among attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.43
Average entropy of the attributes.
300
Number of instances belonging to the least frequent class.
66.67
Percentage of nominal attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.92
Mean kurtosis among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.91
First quartile of entropy among attributes.
1.91
Third quartile of skewness among attributes of the numeric type.
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
476.58
Mean of means among attributes of the numeric type.
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.21
First quartile of kurtosis among attributes of the numeric type.
12.06
Third quartile of standard deviation of attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Average mutual information between the nominal attributes and the target attribute.
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.41
First quartile of means among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
69.93
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
2.04
Standard deviation of the number of distinct values among attributes of the nominal type.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4
Average number of distinct values among the attributes of the nominal type.
-0.27
First quartile of skewness among attributes of the numeric type.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.27
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.92
Mean skewness among attributes of the numeric type.
0.58
First quartile of standard deviation of attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.29
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
70
Percentage of instances belonging to the most frequent class.
407.05
Mean standard deviation of attributes of the numeric type.
1.53
Second quartile (Median) of entropy among attributes.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2

11 tasks

0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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