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waveform-5000

waveform-5000

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jason
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  • artificial OpenML100 study_1 study_123 study_14 study_34 study_37 study_41 study_52 study_7 study_70 uci study_225 study_236 study_293
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Author: Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. Source: [UCI](http://archive.ics.uci.edu/ml/datasets/waveform+database+generator+(version+2)) - 1988 Please cite: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) Waveform Database Generator Generator generating 3 classes of waves. Each class is generated from a combination of 2 of 3 "base" waves. For details, see Breiman,L., Friedman,J.H., Olshen,R.A., and Stone,C.J. (1984). Classification and Regression Trees. Wadsworth International, pp 49-55, 169. Note: There is [an earlier version](http://archive.ics.uci.edu/ml/datasets/Waveform+Database+Generator+(Version+1)) of this dataset that only has 21 attributes (it does not add the 19 noise features). ### Attribute Information 40 attributes describing the waveform, all of which include noise. The latter 19 attributes are all noise attributes with mean 0 and variance 1.

41 features

class (target)nominal3 unique values
0 missing
x22numeric528 unique values
0 missing
x21numeric532 unique values
0 missing
x23numeric517 unique values
0 missing
x24numeric525 unique values
0 missing
x25numeric528 unique values
0 missing
x26numeric524 unique values
0 missing
x27numeric526 unique values
0 missing
x28numeric535 unique values
0 missing
x29numeric528 unique values
0 missing
x30numeric526 unique values
0 missing
x31numeric536 unique values
0 missing
x32numeric531 unique values
0 missing
x33numeric529 unique values
0 missing
x34numeric523 unique values
0 missing
x35numeric538 unique values
0 missing
x36numeric516 unique values
0 missing
x37numeric512 unique values
0 missing
x38numeric521 unique values
0 missing
x39numeric532 unique values
0 missing
x40numeric531 unique values
0 missing
x11numeric749 unique values
0 missing
x2numeric542 unique values
0 missing
x3numeric615 unique values
0 missing
x4numeric692 unique values
0 missing
x5numeric773 unique values
0 missing
x6numeric816 unique values
0 missing
x7numeric891 unique values
0 missing
x8numeric803 unique values
0 missing
x9numeric765 unique values
0 missing
x10numeric714 unique values
0 missing
x1numeric530 unique values
0 missing
x12numeric730 unique values
0 missing
x13numeric769 unique values
0 missing
x14numeric794 unique values
0 missing
x15numeric887 unique values
0 missing
x16numeric817 unique values
0 missing
x17numeric755 unique values
0 missing
x18numeric682 unique values
0 missing
x19numeric606 unique values
0 missing
x20numeric555 unique values
0 missing
Show all 41 features

107 properties

NumberOfInstances
5000
Number of instances (rows) of the dataset.
NumberOfFeatures
41
Number of attributes (columns) of the dataset.
NumberOfClasses
3
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
40
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.33
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.85
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
CfsSubsetEval_DecisionStumpErrRate
0.23
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
CfsSubsetEval_DecisionStumpKappa
0.65
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
CfsSubsetEval_NaiveBayesAUC
0.85
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
CfsSubsetEval_NaiveBayesErrRate
0.23
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
CfsSubsetEval_NaiveBayesKappa
0.65
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
CfsSubsetEval_kNN1NAUC
0.85
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
CfsSubsetEval_kNN1NErrRate
0.23
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
CfsSubsetEval_kNN1NKappa
0.65
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
ClassEntropy
1.58
Entropy of the target attribute values.
DecisionStumpAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.44
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
33.84
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1692
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.15
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
3.32
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.27
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
2.03
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.2
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.9
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
3
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.04
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1.27
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.68
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.03
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.27
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.98
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
33.06
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1653
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
97.56
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
2.44
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.48
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.02
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.05
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.03
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.02
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.02
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
2
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.08
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1.66
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

41 tasks

Supervised Classification on waveform-5000
14815 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on waveform-5000
323 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on waveform-5000
313 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on waveform-5000
184 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on waveform-5000
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on waveform-5000
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Learning Curve on waveform-5000
326 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on waveform-5000
165 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on waveform-5000
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on waveform-5000
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on waveform-5000
0 runs - target_feature: class
Clustering on waveform-5000
0 runs
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Clustering on waveform-5000
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on waveform-5000
1318 runs - target_feature: class
Subgroup Discovery on waveform-5000
1316 runs - target_feature: class
Subgroup Discovery on waveform-5000
1312 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
Subgroup Discovery on waveform-5000
0 runs - target_feature: class
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