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BNG(ionosphere)

BNG(ionosphere)

active ARFF Publicly available Visibility: public Uploaded 06-10-2016 by Jason
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Automated file upload of BNG(ionosphere)

35 features

class (target)nominal2 unique values
0 missing
a19nominal3 unique values
0 missing
a18nominal3 unique values
0 missing
a20nominal3 unique values
0 missing
a21nominal3 unique values
0 missing
a22nominal3 unique values
0 missing
a23nominal3 unique values
0 missing
a24nominal3 unique values
0 missing
a25nominal3 unique values
0 missing
a26nominal3 unique values
0 missing
a27nominal3 unique values
0 missing
a28nominal3 unique values
0 missing
a29nominal3 unique values
0 missing
a30nominal3 unique values
0 missing
a31nominal3 unique values
0 missing
a32nominal3 unique values
0 missing
a33nominal3 unique values
0 missing
a34nominal3 unique values
0 missing
a10nominal3 unique values
0 missing
a02nominal1 unique values
0 missing
a03nominal3 unique values
0 missing
a04nominal3 unique values
0 missing
a05nominal3 unique values
0 missing
a06nominal3 unique values
0 missing
a07nominal3 unique values
0 missing
a08nominal3 unique values
0 missing
a09nominal3 unique values
0 missing
a01nominal3 unique values
0 missing
a11nominal3 unique values
0 missing
a12nominal3 unique values
0 missing
a13nominal3 unique values
0 missing
a14nominal3 unique values
0 missing
a15nominal3 unique values
0 missing
a16nominal3 unique values
0 missing
a17nominal3 unique values
0 missing
Show all 35 features

62 properties

NumberOfInstances
1000000
Number of instances (rows) of the dataset.
NumberOfFeatures
35
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
0
Number of numeric attributes.
NumberOfSymbolicFeatures
35
Number of nominal attributes.
Quartile1MutualInformation
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
2.86
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
1.23
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0.37
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.39
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.44
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.08
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.54
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
ClassEntropy
0.94
Entropy of the target attribute values.
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
14.12
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
64.1
Percentage of instances belonging to the most frequent class.
MajorityClassSize
641025
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1.48
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.28
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
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.28
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
MeanMutualInformation
0.07
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
18.23
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.91
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
-0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
35.9
Percentage of instances belonging to the least frequent class.
MinorityClassSize
358975
Number of instances belonging to the least frequent class.
Show all 62 properties

22 tasks

Supervised Classification on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on BNG(ionosphere)
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(ionosphere)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on BNG(ionosphere)
99 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(ionosphere)
0 runs - estimation_procedure: 50 times Clustering
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