Data
GermanCredit-train

GermanCredit-train

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: Professor Dr. Hans Hofmann Institut f"ur Statistik und "Okonometrie Universit"at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13 Data Set Information: 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. 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). Attribute Information: Attribute 1: (qualitative) Status of existing checking account A11 : ... < 0 xss=removed>= 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 xss=removed xss=removed>= 1000 DM A65 : unknown/ no savings account Attribute 7: (qualitative) Present employment since A71 : unemployed A72 : ... < 1 xss=removed xss=removed>= 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 #autoxgboost #autoweka

21 features

checking_statusnominal4 unique values
0 missing
durationnumeric31 unique values
0 missing
credit_historynominal5 unique values
0 missing
purposenominal10 unique values
0 missing
credit_amountnumeric658 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
classnominal2 unique values
0 missing

62 properties

700
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
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.
-0.31
First quartile of skewness among attributes of the numeric type.
453.33
Mean of means among attributes of the numeric type.
0.56
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.88
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.03
Number of attributes divided by the number of instances.
4
Average number of distinct values among the attributes of the nominal type.
2.99
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.88
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
368.26
Mean standard deviation of attributes of the numeric type.
1.07
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
14.29
Percentage of binary attributes.
1.11
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.37
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
3.88
Maximum kurtosis among attributes of the numeric type.
1.16
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
1.46
Third quartile of kurtosis among attributes of the numeric type.
3108.86
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
33.33
Percentage of numeric attributes.
35.47
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
66.67
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
-0.56
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.86
Third quartile of skewness among attributes of the numeric type.
1.86
Maximum skewness among attributes of the numeric type.
0.37
Minimum standard deviation of attributes of the numeric type.
-1.17
First quartile of kurtosis among attributes of the numeric type.
11.5
Third quartile of standard deviation of attributes of the numeric type.
2551.91
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
1.4
First quartile of means among attributes of the numeric type.
2.04
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
3
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.8
Mean kurtosis among attributes of the numeric type.

16 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - 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
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: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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