Data
BNG(mfeat-fourier,nominal,1000000)

BNG(mfeat-fourier,nominal,1000000)

active ARFF Publicly available Visibility: public Uploaded 08-04-2014 by Jan van Rijn
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77 features

class (target)nominal10 unique values
0 missing
att1nominal3 unique values
0 missing
att2nominal3 unique values
0 missing
att3nominal3 unique values
0 missing
att4nominal3 unique values
0 missing
att5nominal3 unique values
0 missing
att6nominal3 unique values
0 missing
att7nominal3 unique values
0 missing
att8nominal3 unique values
0 missing
att9nominal3 unique values
0 missing
att10nominal3 unique values
0 missing
att11nominal3 unique values
0 missing
att12nominal3 unique values
0 missing
att13nominal3 unique values
0 missing
att14nominal3 unique values
0 missing
att15nominal3 unique values
0 missing
att16nominal3 unique values
0 missing
att17nominal3 unique values
0 missing
att18nominal3 unique values
0 missing
att19nominal3 unique values
0 missing
att20nominal3 unique values
0 missing
att21nominal3 unique values
0 missing
att22nominal3 unique values
0 missing
att23nominal3 unique values
0 missing
att24nominal3 unique values
0 missing
att25nominal3 unique values
0 missing
att26nominal3 unique values
0 missing
att27nominal3 unique values
0 missing
att28nominal3 unique values
0 missing
att29nominal3 unique values
0 missing
att30nominal3 unique values
0 missing
att31nominal3 unique values
0 missing
att32nominal3 unique values
0 missing
att33nominal3 unique values
0 missing
att34nominal3 unique values
0 missing
att35nominal3 unique values
0 missing
att36nominal3 unique values
0 missing
att37nominal3 unique values
0 missing
att38nominal3 unique values
0 missing
att39nominal3 unique values
0 missing
att40nominal3 unique values
0 missing
att41nominal3 unique values
0 missing
att42nominal3 unique values
0 missing
att43nominal3 unique values
0 missing
att44nominal3 unique values
0 missing
att45nominal3 unique values
0 missing
att46nominal3 unique values
0 missing
att47nominal3 unique values
0 missing
att48nominal3 unique values
0 missing
att49nominal3 unique values
0 missing
att50nominal3 unique values
0 missing
att51nominal3 unique values
0 missing
att52nominal3 unique values
0 missing
att53nominal3 unique values
0 missing
att54nominal3 unique values
0 missing
att55nominal3 unique values
0 missing
att56nominal3 unique values
0 missing
att57nominal3 unique values
0 missing
att58nominal3 unique values
0 missing
att59nominal3 unique values
0 missing
att60nominal3 unique values
0 missing
att61nominal3 unique values
0 missing
att62nominal3 unique values
0 missing
att63nominal3 unique values
0 missing
att64nominal3 unique values
0 missing
att65nominal3 unique values
0 missing
att66nominal3 unique values
0 missing
att67nominal3 unique values
0 missing
att68nominal3 unique values
0 missing
att69nominal3 unique values
0 missing
att70nominal3 unique values
0 missing
att71nominal3 unique values
0 missing
att72nominal3 unique values
0 missing
att73nominal3 unique values
0 missing
att74nominal3 unique values
0 missing
att75nominal3 unique values
0 missing
att76nominal3 unique values
0 missing

62 properties

1000000
Number of instances (rows) of the dataset.
77
Number of attributes (columns) of the dataset.
10
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.
77
Number of nominal attributes.
1.23
Average entropy of the attributes.
99773
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.8
Standard deviation of the number of distinct values among attributes of the nominal type.
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.06
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.
0.1
Average class difference between consecutive instances.
0.15
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.
3.32
Entropy of the target attribute values.
7.09
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.24
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
3.09
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
21.87
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.
10.06
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
0.09
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
100595
Number of instances belonging to the most frequent class.
0.94
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
1.56
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.3
Third quartile of entropy among attributes.
Maximum of means among attributes of the numeric type.
0.04
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.6
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
10
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.
0.2
Third quartile of mutual information between the nominal attributes and the target attribute.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
1.16
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
9.98
Percentage of instances belonging to the least frequent class.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.

17 tasks

4 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - 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
44 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
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