Data
credit-g

credit-g

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • credit_scoring mythbusting_1 OpenML-CC18 OpenML100 study_1 study_123 study_14 study_144 study_15 study_20 study_34 study_37 study_41 study_50 study_52 study_7 study_70 study_98 study_99 uci
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Author: Dr. Hans Hofmann Source: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) German Credit data This dataset classifies people described by a set of attributes as good or bad credit risks. This dataset comes with a cost matrix: ``` Good Bad (predicted) Good 0 1 (actual) Bad 5 0 ``` 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). ### Attribute description 1. Status of existing checking account, in Deutsche Mark. 2. Duration in months 3. Credit history (credits taken, paid back duly, delays, critical accounts) 4. Purpose of the credit (car, television,...) 5. Credit amount 6. Status of savings account/bonds, in Deutsche Mark. 7. Present employment, in number of years. 8. Installment rate in percentage of disposable income 9. Personal status (married, single,...) and sex 10. Other debtors / guarantors 11. Present residence since X years 12. Property (e.g. real estate) 13. Age in years 14. Other installment plans (banks, stores) 15. Housing (rent, own,...) 16. Number of existing credits at this bank 17. Job 18. Number of people being liable to provide maintenance for 19. Telephone (yes,no) 20. Foreign worker (yes,no)

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.
0.22
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.
0.57
Average class difference between consecutive instances.
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.
0.72
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
0.66
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.
0.03
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
0.3
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.
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
0.28
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.
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
0.66
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.
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
0.3
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.
0.27
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
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.
0.22
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
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.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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.
0.27
Error rate 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.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
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.
0.27
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.

32 tasks

411311 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
82355 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
3485 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
225 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
205 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
177 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: 1 - target_feature: class
2 runs - estimation_procedure: 33% Holdout set - evaluation_measure: auc - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: AUC - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: credit_amount
353 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
202 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: clusters
0 runs - target_feature: class
1312 runs - target_feature: class
1305 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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