Data
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

62 properties

58000
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
7
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.
9
Number of numeric attributes.
1
Number of nominal attributes.
-21.86
Minimum skewness among attributes of the numeric type.
10
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
8.9
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
4.34
Third quartile of skewness among attributes of the numeric type.
31.69
Maximum skewness among attributes of the numeric type.
0.02
Percentage of instances belonging to the least frequent class.
4.07
First quartile of kurtosis among attributes of the numeric type.
57.24
Third quartile of standard deviation of attributes of the numeric type.
217.6
Maximum standard deviation of attributes of the numeric type.
10
Number of instances belonging to the least frequent class.
0.93
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1817.65
Mean kurtosis among attributes of the numeric type.
-0.77
First quartile of skewness among attributes of the numeric type.
30.21
Mean of means among attributes of the numeric type.
12.67
First quartile of standard deviation of attributes of the numeric type.
0.65
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.96
Entropy of the target attribute values.
7
Average number of distinct values among the attributes of the nominal type.
8.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2.37
Mean skewness among attributes of the numeric type.
34.55
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
48.34
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
78.6
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.1
Second quartile (Median) of skewness among attributes of the numeric type.
45586
Number of instances belonging to the most frequent class.
0.54
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
21.66
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.02
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
7698.22
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
4313.28
Third quartile of kurtosis among attributes of the numeric type.
85.35
Maximum of means among attributes of the numeric type.
7
The minimal number of distinct values among attributes of the nominal type.
90
Percentage of numeric attributes.
49.56
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.

26 tasks

10 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
2 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - 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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