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BNG(ionosphere)

BNG(ionosphere)

active ARFF Publicly available Visibility: public Uploaded 06-10-2016 by Jan van Rijn
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35 features

class (target)nominal2 unique values
0 missing
a01nominal3 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
a10nominal3 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
a18nominal3 unique values
0 missing
a19nominal3 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

62 properties

1000000
Number of instances (rows) of the dataset.
35
Number of attributes (columns) of the dataset.
2
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.
0
Number of numeric attributes.
35
Number of nominal attributes.
Third quartile of means among attributes of the numeric type.
0.28
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of numeric attributes.
0.08
Third quartile of mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
100
Percentage of nominal attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
1.23
First quartile of entropy among attributes.
Third quartile of standard deviation of attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
35.9
Percentage of instances belonging to the least frequent class.
First quartile of kurtosis among attributes of the numeric type.
0.37
Standard deviation of the number of distinct values among attributes of the nominal type.
1.28
Average entropy of the attributes.
358975
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
Mean of means among attributes of the numeric type.
First quartile of skewness among attributes of the numeric type.
0.54
Average class difference between consecutive instances.
0.07
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.
0.94
Entropy of the target attribute values.
18.23
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.39
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
2.91
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
14.12
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
64.1
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
641025
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
1.48
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
2.86
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.44
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

12 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - 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
99 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task