Data
BNG(credit-g)

BNG(credit-g)

active ARFF Publicly available Visibility: public Uploaded 06-10-2016 by Jan van Rijn
0 likes downloaded by 3 people , 3 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Automated file upload of BNG(credit-g)

21 features

class (target)nominal2 unique values
0 missing
checking_statusnominal4 unique values
0 missing
durationnumeric971857 unique values
0 missing
credit_historynominal5 unique values
0 missing
purposenominal11 unique values
0 missing
credit_amountnumeric999900 unique values
0 missing
savings_statusnominal5 unique values
0 missing
employmentnominal5 unique values
0 missing
installment_commitmentnumeric4 unique values
0 missing
personal_statusnominal5 unique values
0 missing
other_partiesnominal3 unique values
0 missing
residence_sincenumeric4 unique values
0 missing
property_magnitudenominal4 unique values
0 missing
agenumeric983864 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

62 properties

1000000
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.
35.63
Third quartile of means among attributes of the numeric type.
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.
33.33
Percentage of numeric attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
11
The maximum number of distinct values among attributes of the nominal type.
-0.51
Minimum skewness among attributes of the numeric type.
66.67
Percentage of nominal attributes.
1.76
Third quartile of skewness among attributes of the numeric type.
1.88
Maximum skewness among attributes of the numeric type.
0.36
Minimum standard deviation of attributes of the numeric type.
0.92
First quartile of entropy among attributes.
12.06
Third quartile of standard deviation of attributes of the numeric type.
2779.72
Maximum standard deviation of attributes of the numeric type.
30.02
Percentage of instances belonging to the least frequent class.
-1.22
First quartile of kurtosis among attributes of the numeric type.
2.28
Standard deviation of the number of distinct values among attributes of the nominal type.
1.44
Average entropy of the attributes.
300226
Number of instances belonging to the least frequent class.
1.43
First quartile of means among attributes of the numeric type.
0.65
Mean kurtosis among attributes of the numeric type.
3
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
467.86
Mean of means among attributes of the numeric type.
-0.26
First quartile of skewness among attributes of the numeric type.
0.58
Average class difference between consecutive instances.
0.02
Average mutual information between the nominal attributes and the target attribute.
0.61
First quartile of standard deviation of attributes of the numeric type.
0.88
Entropy of the target attribute values.
73.03
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.55
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
4.14
Average number of distinct values among the attributes of the nominal type.
0.45
Second quartile (Median) of kurtosis among attributes of the numeric type.
45.19
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.89
Mean skewness among attributes of the numeric type.
2.95
Second quartile (Median) of means among attributes of the numeric type.
69.98
Percentage of instances belonging to the most frequent class.
400.9
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
699774
Number of instances belonging to the most frequent class.
0.24
Minimal entropy among attributes.
1.03
Second quartile (Median) of skewness among attributes of the numeric type.
2.69
Maximum entropy among attributes.
-1.38
Minimum kurtosis among attributes of the numeric type.
14.29
Percentage of binary attributes.
1.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.88
Third quartile of entropy among attributes.
2.67
Maximum kurtosis among attributes of the numeric type.
1.16
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
2.2
Third quartile of kurtosis among attributes of the numeric type.
3210.12
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - 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
99 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task