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SVHN_medium

SVHN_medium

active ARFF Publicly available Visibility: public Uploaded 18-09-2019 by Felicia West
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50% stratified subsample of the original SVHN data

3073 features

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Show first 100 features

62 properties

NumberOfInstances
49644
Number of instances (rows) of the dataset.
NumberOfFeatures
3073
Number of attributes (columns) of the dataset.
NumberOfClasses
10
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
3072
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.97
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.03
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.52
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
113.13
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.3
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
51.44
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.45
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
114.09
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
0.33
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
53.24
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.35
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
119.24
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
0.38
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
54
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
115.65
Mean of means among attributes of the numeric type.
ClassEntropy
3.22
Entropy of the target attribute values.
Dimensionality
0.06
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.
MajorityClassPercentage
19.1
Percentage of instances belonging to the most frequent class.
MajorityClassSize
9480
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.13
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
124.14
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.59
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
57.1
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.41
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
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
10
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.35
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
52.66
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.71
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
108.87
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
10
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.18
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
46.88
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
6.3
Percentage of instances belonging to the least frequent class.
MinorityClassSize
3127
Number of instances belonging to the least frequent class.
Show all 62 properties

11 tasks

Learning Curve on SVHN_medium
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on SVHN_medium
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on SVHN_medium
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
Clustering on SVHN_medium
0 runs - estimation_procedure: 50 times Clustering
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