Data
RandomRBF_0_0

RandomRBF_0_0

active ARFF Publicly available Visibility: public Uploaded 10-04-2014 by Jan van Rijn
0 likes downloaded by 3 people , 3 total downloads 0 issues 0 downvotes
  • concept_drift study_16 study_16
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit

11 features

class (target)nominal5 unique values
0 missing
att1numeric658258 unique values
0 missing
att2numeric680666 unique values
0 missing
att3numeric647587 unique values
0 missing
att4numeric676097 unique values
0 missing
att5numeric644592 unique values
0 missing
att6numeric678621 unique values
0 missing
att7numeric685881 unique values
0 missing
att8numeric675734 unique values
0 missing
att9numeric670341 unique values
0 missing
att10numeric663734 unique values
0 missing

62 properties

1000000
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
5
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.
10
Number of numeric attributes.
1
Number of nominal attributes.
0.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.24
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
Third quartile of entropy among attributes.
0.48
Maximum kurtosis among attributes of the numeric type.
0.39
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.4
Third quartile of kurtosis among attributes of the numeric type.
0.59
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.52
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
5
The minimal number of distinct values among attributes of the nominal type.
90.91
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
5
The maximum number of distinct values among attributes of the nominal type.
-0.35
Minimum skewness among attributes of the numeric type.
9.09
Percentage of nominal attributes.
0.09
Third quartile of skewness among attributes of the numeric type.
0.26
Maximum skewness among attributes of the numeric type.
0.3
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.35
Third quartile of standard deviation of attributes of the numeric type.
0.37
Maximum standard deviation of attributes of the numeric type.
9.27
Percentage of instances belonging to the least frequent class.
-0.08
First quartile of kurtosis 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.
92713
Number of instances belonging to the least frequent class.
0.47
First quartile of means among attributes of the numeric type.
0.15
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.5
Mean of means among attributes of the numeric type.
-0.21
First quartile of skewness among attributes of the numeric type.
0.32
First quartile of standard deviation of attributes of the numeric type.
0.23
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.22
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
5
Average number of distinct values among the attributes of the nominal type.
0.5
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.
-0.03
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
30.01
Percentage of instances belonging to the most frequent class.
0.33
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of skewness among attributes of the numeric type.
300096
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

16 tasks

19 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
279 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