Data
BNG(labor)

BNG(labor)

active ARFF Publicly available Visibility: public Uploaded 28-04-2014 by Jan van Rijn
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17 features

class (target)nominal2 unique values
0 missing
durationnumeric3 unique values
0 missing
wage-increase-first-yearnumeric789115 unique values
0 missing
wage-increase-second-yearnumeric610550 unique values
0 missing
wage-increase-third-yearnumeric173071 unique values
0 missing
cost-of-living-adjustmentnominal3 unique values
0 missing
working-hoursnumeric448499 unique values
0 missing
pensionnominal3 unique values
0 missing
standby-paynumeric96415 unique values
0 missing
shift-differentialnumeric244986 unique values
0 missing
education-allowancenominal2 unique values
0 missing
statutory-holidaysnumeric225993 unique values
0 missing
vacationnominal3 unique values
0 missing
longterm-disability-assistancenominal2 unique values
0 missing
contribution-to-dental-plannominal3 unique values
0 missing
bereavement-assistancenominal2 unique values
0 missing
contribution-to-health-plannominal3 unique values
0 missing

107 properties

1000000
Number of instances (rows) of the dataset.
17
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.
8
Number of numeric attributes.
9
Number of nominal attributes.
1.12
Second quartile (Median) of entropy among attributes.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.84
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
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
64.7
Percentage of instances belonging to the most frequent class.
1.89
Mean standard deviation of attributes of the numeric type.
1.75
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.94
Entropy of the target attribute values.
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
647000
Number of instances belonging to the most frequent class.
0.39
Minimal entropy among attributes.
4.11
Second quartile (Median) of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.57
Maximum entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
0.08
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.17
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
12.97
Maximum kurtosis among attributes of the numeric type.
2.15
Minimum of means among attributes of the numeric type.
0.31
Second quartile (Median) of skewness among attributes of the numeric type.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
38.21
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
23.53
Percentage of binary attributes.
1.37
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.94
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.
0.33
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.
1.36
Third quartile of entropy among attributes.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
7.97
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3
The maximum number of distinct values among attributes of the nominal type.
-2.49
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
4.41
Third quartile of kurtosis among attributes of the numeric type.
0.54
Average class difference between consecutive instances.
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.22
Maximum skewness among attributes of the numeric type.
0.7
Minimum standard deviation of attributes of the numeric type.
47.06
Percentage of numeric attributes.
9.66
Third quartile of means among attributes of the numeric type.
0.97
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.84
Maximum standard deviation of attributes of the numeric type.
35.3
Percentage of instances belonging to the least frequent class.
52.94
Percentage of nominal attributes.
0.19
Third quartile of mutual information between the nominal attributes and the target attribute.
0.07
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.03
Average entropy of the attributes.
353000
Number of instances belonging to the least frequent class.
0.66
First quartile of entropy among attributes.
1.95
Third quartile of skewness among attributes of the numeric type.
0.84
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
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.9
Mean kurtosis among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.42
First quartile of kurtosis among attributes of the numeric type.
3.16
Third quartile of standard deviation of attributes of the numeric type.
0.97
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
8.97
Mean of means among attributes of the numeric type.
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.48
First quartile of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.07
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.12
Average mutual information between the nominal attributes and the target attribute.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.04
First quartile of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.84
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
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
7.76
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4
Number of binary attributes.
-1.28
First quartile of skewness among attributes of the numeric type.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
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
0.53
Standard deviation of the number of distinct values among attributes of the nominal type.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.56
Average number of distinct values among the attributes of the nominal type.
0.94
First quartile of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.07
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.35
Mean skewness among attributes of the numeric type.

15 tasks

22 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
310 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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