Data
epilobee_mortality

epilobee_mortality

in_preparation ARFF Attribution (CC BY) Visibility: public Uploaded 02-07-2017 by Carsten Behring
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Dataset from 'Honey bee Winter mortality 2012-2014 - Epilobee analysis'

38 features

ID_api (row identifier)string4758 unique values
0 missing
Winter_mortalitynumeric80 unique values
0 missing
Agestring4 unique values
0 missing
Activitystring3 unique values
0 missing
Beekeep_forstring3 unique values
0 missing
Qualifstring2 unique values
0 missing
Trainingstring2 unique values
0 missing
Coop_treatstring2 unique values
0 missing
Bee_population_sizestring6 unique values
0 missing
Countrystring16 unique values
0 missing
Apiary_Sizestring5 unique values
0 missing
Productionstring7 unique values
0 missing
Apiarist_bookstring2 unique values
0 missing
Org_memberstring2 unique values
0 missing
Continuestring2 unique values
0 missing
Breedstring8 unique values
0 missing
Chronic_Depopstring2 unique values
0 missing
ClinSign_Broodstring2 unique values
0 missing
ClinSign_Honeybeesstring2 unique values
0 missing
H_Rate_ColMortalitystring2 unique values
0 missing
H_Rate_HoneyMortalitystring2 unique values
0 missing
OtherEventstring2 unique values
0 missing
VarroaMitesstring2 unique values
0 missing
QueenProblemsstring2 unique values
0 missing
Managementstring8 unique values
0 missing
Swarm_boughtstring5 unique values
0 missing
Swarm_producedstring5 unique values
0 missing
Queen_boughtstring5 unique values
0 missing
Queen_producedstring5 unique values
0 missing
MidSeason_Targetstring5 unique values
0 missing
Environmentstring7 unique values
0 missing
VarroosisV1string2 unique values
0 missing
ChronicParalysisV1string2 unique values
0 missing
AmericanFoulbroodV1string2 unique values
0 missing
NosemosisV1string2 unique values
0 missing
EuropeanFoulbroodV1string2 unique values
0 missing
Migrationstring2 unique values
0 missing
Mergerstring2 unique values
0 missing
Programstring2 unique values
0 missing

62 properties

4758
Number of instances (rows) of the dataset.
38
Number of attributes (columns) of the dataset.
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.
1
Number of numeric attributes.
0
Number of nominal attributes.
2.24
First quartile of skewness among attributes of the numeric type.
11.45
Mean of means among attributes of the numeric type.
19.66
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
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.
5.22
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
11.45
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.
2.24
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
19.66
Mean standard deviation of attributes of the numeric type.
2.24
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
19.66
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
5.22
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
5.22
Maximum kurtosis among attributes of the numeric type.
11.45
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
5.22
Third quartile of kurtosis among attributes of the numeric type.
11.45
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2.63
Percentage of numeric attributes.
11.45
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
2.24
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.24
Third quartile of skewness among attributes of the numeric type.
2.24
Maximum skewness among attributes of the numeric type.
19.66
Minimum standard deviation of attributes of the numeric type.
5.22
First quartile of kurtosis among attributes of the numeric type.
19.66
Third quartile of standard deviation of attributes of the numeric type.
19.66
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
11.45
First quartile of means among attributes of the numeric type.
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.
5.22
Mean kurtosis among attributes of the numeric type.

8 tasks

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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