Data
BNG(credit-a)

BNG(credit-a)

active ARFF Publicly available Visibility: public Uploaded 29-04-2014 by Jan van Rijn
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16 features

class (target)nominal2 unique values
0 missing
A1nominal2 unique values
0 missing
A2numeric983829 unique values
0 missing
A3numeric941217 unique values
0 missing
A4nominal4 unique values
0 missing
A5nominal3 unique values
0 missing
A6nominal14 unique values
0 missing
A7nominal9 unique values
0 missing
A8numeric811205 unique values
0 missing
A9nominal2 unique values
0 missing
A10nominal2 unique values
0 missing
A11numeric320697 unique values
0 missing
A12nominal2 unique values
0 missing
A13nominal3 unique values
0 missing
A14numeric997141 unique values
0 missing
A15numeric581622 unique values
0 missing

107 properties

1000000
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.
6
Number of numeric attributes.
10
Number of nominal attributes.
0.98
Second quartile (Median) of entropy among attributes.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.75
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
0.16
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.4
Percentage of instances belonging to the most frequent class.
911.14
Mean standard deviation of attributes of the numeric type.
3.22
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
554008
Number of instances belonging to the most frequent class.
0.56
Minimal entropy among attributes.
18.04
Second quartile (Median) of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.52
Maximum entropy among attributes.
0.57
Minimum kurtosis among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
16.2
Maximum kurtosis among attributes of the numeric type.
2.2
Minimum of means among attributes of the numeric type.
1.78
Second quartile (Median) of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1041.65
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
31.25
Percentage of binary attributes.
8.46
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.4
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
1.58
Third quartile of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
11.8
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
14
The maximum number of distinct values among attributes of the nominal type.
1.08
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
10.88
Third quartile of kurtosis among attributes of the numeric type.
0.51
Average class difference between consecutive instances.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.87
Maximum skewness among attributes of the numeric type.
3.34
Minimum standard deviation of attributes of the numeric type.
37.5
Percentage of numeric attributes.
396.7
Third quartile of means among attributes of the numeric type.
0.92
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
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5269.33
Maximum standard deviation of attributes of the numeric type.
44.6
Percentage of instances belonging to the least frequent class.
62.5
Percentage of nominal attributes.
0.12
Third quartile of mutual information between the nominal attributes and the target attribute.
0.12
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
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.31
Average entropy of the attributes.
445992
Number of instances belonging to the least frequent class.
0.84
First quartile of entropy among attributes.
2.72
Third quartile of skewness among attributes of the numeric type.
0.75
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
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.55
Mean kurtosis among attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.86
First quartile of kurtosis among attributes of the numeric type.
1446.62
Third quartile of standard deviation of attributes of the numeric type.
0.92
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
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
210.68
Mean of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.36
First quartile of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.12
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
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.08
Average mutual information between the nominal attributes and the target attribute.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.75
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
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
14.58
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5
Number of binary attributes.
1.27
First quartile of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.92
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
4.03
Standard deviation of the number of distinct values among attributes of the nominal type.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.3
Average number of distinct values among the attributes of the nominal type.
4.5
First quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.12
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.92
Mean skewness among attributes of the numeric type.

15 tasks

20 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 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
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
306 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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