Data
BNG(labor,nominal,1000000)

BNG(labor,nominal,1000000)

active ARFF Publicly available Visibility: public Uploaded 08-04-2014 by Jan van Rijn
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17 features

class (target)nominal2 unique values
0 missing
durationnominal3 unique values
0 missing
wage-increase-first-yearnominal3 unique values
0 missing
wage-increase-second-yearnominal3 unique values
0 missing
wage-increase-third-yearnominal3 unique values
0 missing
cost-of-living-adjustmentnominal3 unique values
0 missing
working-hoursnominal3 unique values
0 missing
pensionnominal3 unique values
0 missing
standby-paynominal3 unique values
0 missing
shift-differentialnominal3 unique values
0 missing
education-allowancenominal2 unique values
0 missing
statutory-holidaysnominal3 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

62 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.
0
Number of numeric attributes.
17
Number of nominal attributes.
1.03
Average entropy of the attributes.
354480
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.44
Standard deviation of the number of distinct values among attributes of the nominal type.
Mean kurtosis among attributes of the numeric type.
4
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
Mean of means among attributes of the numeric type.
First quartile of skewness among attributes of the numeric type.
0.54
Average class difference between consecutive instances.
0.09
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.
0.94
Entropy of the target attribute values.
11.04
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.12
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
2.76
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
10.93
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
64.55
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
645520
Number of instances belonging to the most frequent class.
0.42
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
1.58
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
23.53
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.38
Third quartile of entropy among attributes.
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.
Third quartile of kurtosis among attributes of the numeric type.
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 numeric attributes.
Third quartile of means among attributes of the numeric type.
3
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
100
Percentage of nominal attributes.
0.15
Third quartile of mutual information between the nominal attributes and the target attribute.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.7
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
35.45
Percentage of instances belonging to the least frequent class.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.

16 tasks

25 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
1 runs - estimation_procedure: 5 times 2-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 times 10-fold Crossvalidation - 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
45 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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