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meta_ensembles.arff

meta_ensembles.arff

active ARFF Publicly available Visibility: public Uploaded 13-05-2014 by Jan van Rijn
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63 features

class (target)nominal4 unique values
0 missing
openml_task_idnumeric74 unique values
0 missing
meta_REPTreeDepth2ErrRatenumeric68 unique values
0 missing
meta_J48.00001.ErrRatenumeric65 unique values
0 missing
meta_NBErrRatenumeric67 unique values
0 missing
meta_MeanMutualInformationnumeric56 unique values
0 missing
meta_NBAUCnumeric73 unique values
0 missing
meta_DecisionStumpKappanumeric67 unique values
0 missing
meta_HoeffdingDDM.warningsnumeric11 unique values
0 missing
meta_NoiseToSignalRationumeric56 unique values
0 missing
meta_RandomTreeDepth3AUC_K=0numeric73 unique values
0 missing
meta_PercentageOfNumericAttsnumeric29 unique values
0 missing
meta_EquivalentNumberOfAttsnumeric56 unique values
0 missing
meta_HoeffdingDDM.changesnumeric5 unique values
0 missing
meta_ClassEntropynumeric60 unique values
0 missing
meta_NaiveBayesDdm.changesnumeric7 unique values
0 missing
meta_NumMissingValuesnumeric2 unique values
0 missing
meta_NumNominalAttsnumeric34 unique values
0 missing
meta_REPTreeDepth3AUCnumeric72 unique values
0 missing
meta_MeanAttributeEntropynumeric38 unique values
0 missing
meta_MeanKurtosisOfNumericAttsnumeric36 unique values
0 missing
meta_REPTreeDepth3Kappanumeric71 unique values
0 missing
meta_J48.001.ErrRatenumeric68 unique values
0 missing
meta_NumNumericAttsnumeric20 unique values
0 missing
meta_ClassCountnumeric10 unique values
0 missing
meta_J48.00001.AUCnumeric70 unique values
0 missing
meta_PercentageOfBinaryAttsnumeric25 unique values
0 missing
meta_DecisionStumpAUCnumeric73 unique values
0 missing
meta_RandomTreeDepth1AUC_K=0numeric73 unique values
0 missing
meta_REPTreeDepth2Kappanumeric71 unique values
0 missing
meta_PositivePercentagenumeric51 unique values
0 missing
meta_J48.0001.ErrRatenumeric65 unique values
0 missing
meta_MinNominalAttDistinctValuesnumeric6 unique values
0 missing
meta_RandomTreeDepth2AUC_K=0numeric73 unique values
0 missing
meta_MeanMeansOfNumericAttsnumeric36 unique values
0 missing
meta_J48.0001.kappanumeric66 unique values
0 missing
meta_MeanNominalAttDistinctValuesnumeric40 unique values
0 missing
meta_J48.00001.kappanumeric65 unique values
0 missing
meta_PercentageOfNominalAttsnumeric41 unique values
0 missing
meta_REPTreeDepth1Kappanumeric67 unique values
0 missing
meta_NegativePercentagenumeric63 unique values
0 missing
meta_NumBinaryAttsnumeric16 unique values
0 missing
meta_NaiveBayesDdm.warningsnumeric13 unique values
0 missing
meta_MaxNominalAttDistinctValuesnumeric16 unique values
0 missing
meta_PercentageOfMissingValuesnumeric2 unique values
0 missing
meta_J48.001.AUCnumeric71 unique values
0 missing
meta_J48.0001.AUCnumeric71 unique values
0 missing
meta_NBKappanumeric73 unique values
0 missing
meta_REPTreeDepth1AUCnumeric70 unique values
0 missing
meta_NaiveBayesAdwin.changesnumeric9 unique values
0 missing
meta_REPTreeDepth3ErrRatenumeric69 unique values
0 missing
meta_DecisionStumpErrRatenumeric68 unique values
0 missing
meta_MeanStdDevOfNumericAttsnumeric36 unique values
0 missing
meta_Dimensionalitynumeric29 unique values
0 missing
meta_REPTreeDepth2AUCnumeric72 unique values
0 missing
meta_StdvNominalAttDistinctValuesnumeric37 unique values
0 missing
meta_HoeffdingAdwin.changesnumeric7 unique values
0 missing
meta_MeanSkewnessOfNumericAttsnumeric36 unique values
0 missing
meta_IncompleteInstanceCountnumeric2 unique values
0 missing
meta_DefaultAccuracynumeric63 unique values
0 missing
meta_REPTreeDepth1ErrRatenumeric66 unique values
0 missing
meta_J48.001.kappanumeric66 unique values
0 missing
meta_NumAttributesnumeric29 unique values
0 missing

107 properties

74
Number of instances (rows) of the dataset.
63
Number of attributes (columns) of the dataset.
4
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.
62
Number of numeric attributes.
1
Number of nominal attributes.
0.6
Second quartile (Median) of skewness among attributes of the numeric type.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1353.67
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
1.25
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.85
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
-5.96
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
10.04
Third quartile of kurtosis among attributes of the numeric type.
0.36
Average class difference between consecutive instances.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
8.6
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
98.41
Percentage of numeric attributes.
7.83
Third quartile of means among attributes of the numeric type.
0.62
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.54
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
11149.82
Maximum standard deviation of attributes of the numeric type.
6.76
Percentage of instances belonging to the least frequent class.
1.59
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.5
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
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
5
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
2.6
Third quartile of skewness among attributes of the numeric type.
0.18
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
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
13.39
Mean kurtosis among attributes of the numeric type.
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.44
First quartile of kurtosis among attributes of the numeric type.
16.58
Third quartile of standard deviation of attributes of the numeric type.
0.62
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.54
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
54.72
Mean of means among attributes of the numeric type.
0.51
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.45
First quartile of means among attributes of the numeric type.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
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
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.18
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
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
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.
-0.23
First quartile of skewness among attributes of the numeric type.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.62
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.
0.54
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4
Average number of distinct values among the attributes of the nominal type.
0.21
First quartile of standard deviation of attributes of the numeric type.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.5
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.75
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.18
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
0.51
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
60.81
Percentage of instances belonging to the most frequent class.
309.37
Mean standard deviation of attributes of the numeric type.
0.09
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.54
Entropy of the target attribute values.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
45
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.75
Second quartile (Median) of means among attributes of the numeric type.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.64
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.47
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
74
Maximum kurtosis among attributes of the numeric type.
-26.74
Minimum of means among attributes of the numeric type.

25 tasks

365 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
305 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
174 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
74 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
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
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