Data
GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1

GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1

deactivated ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1-pmlb

21 features

class (target)nominal2 unique values
0 missing
N0nominal2 unique values
0 missing
N1nominal3 unique values
0 missing
N2nominal3 unique values
0 missing
N3nominal3 unique values
0 missing
N4nominal3 unique values
0 missing
N5nominal3 unique values
0 missing
N6nominal3 unique values
0 missing
N7nominal3 unique values
0 missing
N8nominal3 unique values
0 missing
N9nominal3 unique values
0 missing
N10nominal2 unique values
0 missing
N11nominal3 unique values
0 missing
N12nominal3 unique values
0 missing
N13nominal3 unique values
0 missing
N14nominal3 unique values
0 missing
N15nominal3 unique values
0 missing
N16nominal3 unique values
0 missing
N17nominal3 unique values
0 missing
P1nominal3 unique values
0 missing
P2nominal3 unique values
0 missing

62 properties

1600
Number of instances (rows) of the dataset.
21
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.
21
Number of nominal attributes.
1
Entropy of the target attribute values.
1870.29
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.19
Second quartile (Median) of entropy among attributes.
0.01
Number of attributes divided by the number of instances.
2.86
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
1708.23
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.
50
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
800
Number of instances belonging to the most frequent class.
0.14
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
1.5
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
14.29
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.
1.45
Third quartile of entropy among attributes.
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.
Third quartile of kurtosis among attributes of the numeric type.
0
Maximum mutual information between the nominal attributes and the target attribute.
2
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.
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.
0
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.
0.93
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.
50
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.
1.1
Average entropy of the attributes.
800
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.36
Standard deviation of the number of distinct values among attributes of the nominal type.
Mean kurtosis among attributes of the numeric type.
3
Number of binary attributes.
0
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.
1
Average class difference between consecutive instances.
0
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.

21 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - 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
Define a new task