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shuttle

shuttle

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Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Shuttle)) Donor: Jason Catlett Basser Department of Computer Science, University of Sydney, N.S.W., Australia Data Set Information: Approximately 80% of the data belongs to class 1. Therefore the default accuracy is about 80%. The aim here is to obtain an accuracy of 99 - 99.9%. The examples in the original dataset were in time order, and this time order could presumably be relevant in classification. However, this was not deemed relevant for StatLog purposes, so the order of the examples in the original dataset was randomised, and a portion of the original dataset removed for validation purposes. Attribute Information: The shuttle dataset contains 9 attributes all of which are numerical. The first one being time. The last column is the class which has been coded as follows : 1 Rad Flow 2 Fpv Close 3 Fpv Open 4 High 5 Bypass 6 Bpv Close 7 Bpv Open Relevant Papers: N/A

10 features

class (target)nominal7 unique values
0 missing
A1numeric76 unique values
0 missing
A2numeric206 unique values
0 missing
A3numeric51 unique values
0 missing
A4numeric137 unique values
0 missing
A5numeric54 unique values
0 missing
A6numeric299 unique values
0 missing
A7numeric86 unique values
0 missing
A8numeric123 unique values
0 missing
A9numeric77 unique values
0 missing
Show all 10 features

62 properties

NumberOfInstances
58000
Number of instances (rows) of the dataset.
NumberOfFeatures
10
Number of attributes (columns) of the dataset.
NumberOfClasses
7
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
9
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
90
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
10
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
4.07
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.93
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
-0.77
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
12.67
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
8.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
34.55
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
1.1
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
21.66
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
4313.28
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
49.56
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
4.34
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
57.24
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.65
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
30.21
Mean of means among attributes of the numeric type.
ClassEntropy
0.96
Entropy of the target attribute values.
Dimensionality
0
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
78.6
Percentage of instances belonging to the most frequent class.
MajorityClassSize
45586
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
7698.22
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
85.35
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
7
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
31.69
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
217.6
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1817.65
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
7
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
2.37
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
48.34
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
0.54
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.02
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
7
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-21.86
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
8.9
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.02
Percentage of instances belonging to the least frequent class.
MinorityClassSize
10
Number of instances belonging to the least frequent class.
Show all 62 properties

26 tasks

Supervised Classification on shuttle
10 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on shuttle
2 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on shuttle
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on shuttle
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
Supervised Classification on shuttle
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on shuttle
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on shuttle
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on shuttle
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
Clustering on shuttle
0 runs - estimation_procedure: 50 times Clustering
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