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

62 properties

2924
Number of instances (rows) of the dataset.
9
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.
7
Number of numeric attributes.
2
Number of nominal attributes.
-0.6
First quartile of skewness among attributes of the numeric type.
0.38
Mean of means among attributes of the numeric type.
0.1
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.
-0.01
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
14.5
Average number of distinct values among the attributes of the nominal type.
0.36
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.
0.74
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.
0.17
Mean standard deviation of attributes of the numeric type.
0.53
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.
0.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.16
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
100.79
Maximum kurtosis among attributes of the numeric type.
0.14
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
0.61
Third quartile of kurtosis among attributes of the numeric type.
0.83
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
77.78
Percentage of numeric attributes.
0.53
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
22.22
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
26
The maximum number of distinct values among attributes of the nominal type.
-0.63
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.66
Third quartile of skewness among attributes of the numeric type.
4.13
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
-0.16
First quartile of kurtosis among attributes of the numeric type.
0.22
Third quartile of standard deviation of attributes of the numeric type.
0.49
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.
0.18
First quartile of means among attributes of the numeric type.
16.26
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.
14.48
Mean kurtosis among attributes of the numeric type.

17 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Custom Holdout - 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
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
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