Data
abalone

abalone

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani
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Author: Source: [original](http://www.openml.org/d/183) - UCI Please cite: * Abstract: A 3-class version of abalone dataset. * Sources: (a) 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) (b) 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

9 features

Class (target)nominal3 unique values
0 missing
V1nominal3 unique values
0 missing
V2numeric134 unique values
0 missing
V3numeric111 unique values
0 missing
V4numeric51 unique values
0 missing
V5numeric2429 unique values
0 missing
V6numeric1515 unique values
0 missing
V7numeric880 unique values
0 missing
V8numeric926 unique values
0 missing

108 properties

4177
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
3
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.
1.58
Second quartile (Median) of entropy among attributes.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.58
Entropy of the target attribute values.
0.43
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
34.64
Percentage of instances belonging to the most frequent class.
1.58
Minimal entropy among attributes.
0.08
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1447
Number of instances belonging to the most frequent class.
-0.05
Minimum kurtosis among attributes of the numeric type.
0.36
Second quartile (Median) of means among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.43
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.58
Maximum entropy among attributes.
0.14
Minimum of means among attributes of the numeric type.
0.2
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
76.03
Maximum kurtosis among attributes of the numeric type.
0.2
Minimal mutual information between the nominal attributes and the target attribute.
0.59
Second quartile (Median) of skewness among attributes of the numeric type.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.35
The predictive accuracy obtained by always predicting the majority class.
0.83
Maximum of means among attributes of the numeric type.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.12
DataQuality extracted from Fantail Library
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.2
Maximum mutual information between the nominal attributes and the target attribute.
-0.64
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.58
Third quartile of entropy among attributes.
0.58
Average class difference between consecutive instances.
0.44
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
8.06
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3
The maximum number of distinct values among attributes of the nominal type.
0.04
DataQuality extracted from Fantail Library
0
Percentage of missing values.
0.6
Third quartile of kurtosis among attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.13
Maximum skewness among attributes of the numeric type.
0.32
Percentage of instances belonging to the least frequent class.
77.78
Percentage of numeric attributes.
0.52
Third quartile of means among attributes of the numeric type.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.49
DataQuality extracted from Fantail Library
1323
Number of instances belonging to the least frequent class.
22.22
Percentage of nominal attributes.
0.2
Third quartile of mutual information between the nominal attributes and the target attribute.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.58
Average entropy of the attributes.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.58
First quartile of entropy among attributes.
0.72
DataQuality extracted from Fantail Library
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
11.03
Mean kurtosis among attributes of the numeric type.
0.42
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.02
First quartile of kurtosis among attributes of the numeric type.
0.22
DataQuality extracted from Fantail Library
0.42
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.38
Mean of means among attributes of the numeric type.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.18
First quartile of means among attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.2
Average mutual information between the nominal attributes and the target attribute.
7.04
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.2
First quartile of mutual information between the nominal attributes and the target attribute.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
0
Number of binary attributes.
-0.61
First quartile of skewness among attributes of the numeric type.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.44
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.62
Mean skewness among attributes of the numeric type.
0.1
DataQuality extracted from Fantail Library
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.17
Mean standard deviation of attributes of the numeric type.

4 tasks

45 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
3 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
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