Data
Abalone-train

Abalone-train

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: Data comes from an original (non-machine-learning) study: Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994) "The Population Biology of Abalone (_Haliotis_ species) in Tasmania. I. Blacklip Abalone (_H. rubra_) from the North Coast and Islands of Bass Strait", Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288) Original Owners of Database: Marine Resources Division Marine Research Laboratories - Taroona Department of Primary Industry and Fisheries, Tasmania GPO Box 619F, Hobart, Tasmania 7001, Australia (contact: Warwick Nash +61 02 277277, wnash '@' dpi.tas.gov.au) Donor of Database: Sam Waugh (Sam.Waugh '@' cs.utas.edu.au) Department of Computer Science, University of Tasmania GPO Box 252C, Hobart, Tasmania 7001, Australia Data Set Information: Predicting the age of abalone from physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem. From the original data examples with missing values were removed (the majority having the predicted value missing), and the ranges of the continuous values have been scaled for use with an ANN (by dividing by 200). Attribute Information: Given is the attribute name, attribute type, the measurement unit and a brief description. The number of rings is the value to predict: either as a continuous value or as a classification problem. Name / Data Type / Measurement Unit / Description ----------------------------- Sex / nominal / -- / M, F, and I (infant) Length / continuous / mm / Longest shell measurement Diameter / continuous / mm / perpendicular to length Height / continuous / mm / with meat in shell Whole weight / continuous / grams / whole abalone Shucked weight / continuous / grams / weight of meat Viscera weight / continuous / grams / gut weight (after bleeding) Shell weight / continuous / grams / after being dried Rings / integer / -- / +1.5 gives the age in years The readme file contains attribute statistics. #autoxgboost #autoweka

9 features

sexnominal3 unique values
0 missing
lengthnumeric131 unique values
0 missing
diameternumeric107 unique values
0 missing
heightnumeric49 unique values
0 missing
wholeweightnumeric1967 unique values
0 missing
shuckedweightnumeric1339 unique values
0 missing
visceraweightnumeric829 unique values
0 missing
shellweightnumeric805 unique values
0 missing
classnominal26 unique values
0 missing
Show all 9 features

62 properties

NumberOfInstances
2924
Number of instances (rows) of the dataset.
NumberOfFeatures
9
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
7
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
ClassEntropy
Entropy of the target attribute values.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
MeanNominalAttDistinctValues
14.5
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
-0.01
Second quartile (Median) of kurtosis among attributes of the numeric type.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanSkewnessOfNumericAtts
0.74
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.36
Second quartile (Median) of means among attributes of the numeric type.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
0.17
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
0.53
Second quartile (Median) of skewness among attributes of the numeric type.
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-0.16
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
0.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
100.79
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.14
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
0.83
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
0.61
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
77.78
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
0.53
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
26
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.63
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
22.22
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxSkewnessOfNumericAtts
4.13
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.04
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
0.66
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.49
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
-0.16
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.22
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
0.18
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
16.26
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanKurtosisOfNumericAtts
14.48
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
0.38
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
-0.6
First quartile of skewness among attributes of the numeric type.
AutoCorrelation
Average class difference between consecutive instances.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
0.1
First quartile of standard deviation of attributes of the numeric type.
Show all 62 properties

20 tasks

Supervised Classification on Abalone-train
0 runs - estimation_procedure: Custom Holdout - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on Abalone-train
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Learning Curve on Abalone-train
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Abalone-train
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Abalone-train
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Abalone-train
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Abalone-train
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Abalone-train
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Abalone-train
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on Abalone-train
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
Clustering on Abalone-train
0 runs - estimation_procedure: 50 times Clustering
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