Data
BNG(waveform-5000,nominal,1000000)

BNG(waveform-5000,nominal,1000000)

active ARFF Publicly available Visibility: public Uploaded 09-04-2014 by Jan van Rijn
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41 features

class (target)nominal3 unique values
0 missing
x1nominal3 unique values
0 missing
x2nominal3 unique values
0 missing
x3nominal3 unique values
0 missing
x4nominal3 unique values
0 missing
x5nominal3 unique values
0 missing
x6nominal3 unique values
0 missing
x7nominal3 unique values
0 missing
x8nominal3 unique values
0 missing
x9nominal3 unique values
0 missing
x10nominal3 unique values
0 missing
x11nominal3 unique values
0 missing
x12nominal3 unique values
0 missing
x13nominal3 unique values
0 missing
x14nominal3 unique values
0 missing
x15nominal3 unique values
0 missing
x16nominal3 unique values
0 missing
x17nominal3 unique values
0 missing
x18nominal3 unique values
0 missing
x19nominal3 unique values
0 missing
x20nominal3 unique values
0 missing
x21nominal3 unique values
0 missing
x22nominal3 unique values
0 missing
x23nominal3 unique values
0 missing
x24nominal3 unique values
0 missing
x25nominal3 unique values
0 missing
x26nominal3 unique values
0 missing
x27nominal3 unique values
0 missing
x28nominal3 unique values
0 missing
x29nominal3 unique values
0 missing
x30nominal3 unique values
0 missing
x31nominal3 unique values
0 missing
x32nominal3 unique values
0 missing
x33nominal3 unique values
0 missing
x34nominal3 unique values
0 missing
x35nominal3 unique values
0 missing
x36nominal3 unique values
0 missing
x37nominal3 unique values
0 missing
x38nominal3 unique values
0 missing
x39nominal3 unique values
0 missing
x40nominal3 unique values
0 missing

107 properties

1000000
Number of instances (rows) of the dataset.
41
Number of attributes (columns) of the dataset.
3
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.
41
Number of nominal attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.83
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.
0.24
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
1.23
Third quartile of entropy among attributes.
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
21.65
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.
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.33
Average class difference between consecutive instances.
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.93
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
33.05
Percentage of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.17
Third quartile of mutual information between the nominal attributes and the target attribute.
0.17
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
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.12
Average entropy of the attributes.
330548
Number of instances belonging to the least frequent class.
0.99
First quartile of entropy among attributes.
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
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.93
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
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
0.17
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
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.07
Average mutual information between the nominal attributes and the target attribute.
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.17
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
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
14.35
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of skewness among attributes of the numeric type.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.93
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
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
0.17
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
1.08
Second quartile (Median) of entropy among attributes.
0.17
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
0.25
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
33.78
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.58
Entropy of the target attribute values.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
337805
Number of instances belonging to the most frequent class.
0.9
Minimal entropy among attributes.
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
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.38
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.

26 tasks

19 runs - estimation_procedure: 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 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 5 times 2-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
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
44 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
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