Data
crx

crx

deactivated ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Duplicate of credit-approval dataset: https://www.openml.org/d/29

16 features

class (target)nominal2 unique values
0 missing
A1nominal3 unique values
0 missing
A2numeric350 unique values
0 missing
A3numeric215 unique values
0 missing
A4nominal4 unique values
0 missing
A5nominal4 unique values
0 missing
A6numeric15 unique values
0 missing
A7nominal10 unique values
0 missing
A8numeric132 unique values
0 missing
A9nominal2 unique values
0 missing
A10nominal2 unique values
0 missing
A11numeric23 unique values
0 missing
A12nominal2 unique values
0 missing
A13nominal3 unique values
0 missing
A14numeric171 unique values
0 missing
A15numeric240 unique values
0 missing

62 properties

690
Number of instances (rows) of the dataset.
16
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.
9
Number of nominal attributes.
177.23
Mean of means among attributes of the numeric type.
0.41
First quartile of skewness among attributes of the numeric type.
0.97
Average class difference between consecutive instances.
0.09
Average mutual information between the nominal attributes and the target attribute.
4.11
First quartile of standard deviation of attributes of the numeric type.
0.99
Entropy of the target attribute values.
10.52
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.99
Second quartile (Median) of entropy among attributes.
0.02
Number of attributes divided by the number of instances.
3.56
Average number of distinct values among the attributes of the nominal type.
2.27
Second quartile (Median) of kurtosis among attributes of the numeric type.
11.23
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.44
Mean skewness among attributes of the numeric type.
6.4
Second quartile (Median) of means among attributes of the numeric type.
55.51
Percentage of instances belonging to the most frequent class.
768.34
Mean standard deviation of attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
383
Number of instances belonging to the most frequent class.
0.5
Minimal entropy among attributes.
1.49
Second quartile (Median) of skewness among attributes of the numeric type.
1.88
Maximum entropy among attributes.
-1.24
Minimum kurtosis among attributes of the numeric type.
25
Percentage of binary attributes.
4.98
Second quartile (Median) of standard deviation of attributes of the numeric type.
214.67
Maximum kurtosis among attributes of the numeric type.
2.22
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1
Third quartile of entropy among attributes.
1017.39
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.
50.83
Third quartile of kurtosis among attributes of the numeric type.
0.43
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
43.75
Percentage of numeric attributes.
150.53
Third quartile of means among attributes of the numeric type.
10
The maximum number of distinct values among attributes of the nominal type.
0.31
Minimum skewness among attributes of the numeric type.
56.25
Percentage of nominal attributes.
0.13
Third quartile of mutual information between the nominal attributes and the target attribute.
13.14
Maximum skewness among attributes of the numeric type.
3.35
Minimum standard deviation of attributes of the numeric type.
0.88
First quartile of entropy among attributes.
5.15
Third quartile of skewness among attributes of the numeric type.
5210.1
Maximum standard deviation of attributes of the numeric type.
44.49
Percentage of instances belonging to the least frequent class.
-0.95
First quartile of kurtosis among attributes of the numeric type.
96.19
Third quartile of standard deviation of attributes of the numeric type.
1.02
Average entropy of the attributes.
307
Number of instances belonging to the least frequent class.
2.4
First quartile of means among attributes of the numeric type.
2.55
Standard deviation of the number of distinct values among attributes of the nominal type.
39.42
Mean kurtosis among attributes of the numeric type.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.

21 tasks

31 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
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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task