Data
abalone

abalone

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Author: Source: Unknown - Please cite: 1. Title of Database: Abalone data 2. 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 (c) Date received: December 1995 3. Past Usage: Sam Waugh (1995) "Extending and benchmarking Cascade-Correlation", PhD thesis, Computer Science Department, University of Tasmania. -- Test set performance (final 1044 examples, first 3133 used for training): 24.86% Cascade-Correlation (no hidden nodes) 26.25% Cascade-Correlation (5 hidden nodes) 21.5% C4.5 0.0% Linear Discriminate Analysis 3.57% k=5 Nearest Neighbour (Problem encoded as a classification task) -- Data set samples are highly overlapped. Further information is required to separate completely using affine combinations. Other restrictions to data set examined. David Clark, Zoltan Schreter, Anthony Adams "A Quantitative Comparison of Dystal and Backpropagation", submitted to the Australian Conference on Neural Networks (ACNN'96). Data set treated as a 3-category classification problem (grouping ring classes 1-8, 9 and 10, and 11 on). -- Test set performance (3133 training, 1044 testing as above): 64% Backprop 55% Dystal -- Previous work (Waugh, 1995) on same data set: 61.40% Cascade-Correlation (no hidden nodes) 65.61% Cascade-Correlation (5 hidden nodes) 59.2% C4.5 32.57% Linear Discriminate Analysis 62.46% k=5 Nearest Neighbour 4. Relevant Information Paragraph: 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). 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) 5. Number of Instances: 4177 6. Number of Attributes: 8 7. 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 Meas. 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 Statistics for numeric domains: Length Diam Height Whole Shucked Viscera Shell Rings Min 0.075 0.055 0.000 0.002 0.001 0.001 0.002 1 Max 0.815 0.650 1.130 2.826 1.488 0.760 1.005 29 Mean 0.524 0.408 0.140 0.829 0.359 0.181 0.239 9.934 SD 0.120 0.099 0.042 0.490 0.222 0.110 0.139 3.224 Correl 0.557 0.575 0.557 0.540 0.421 0.504 0.628 1.0 8. Missing Attribute Values: None 9. Class Distribution: Class Examples ----- -------- 1 1 2 1 3 15 4 57 5 115 6 259 7 391 8 568 9 689 10 634 11 487 12 267 13 203 14 126 15 103 16 67 17 58 18 42 19 32 20 26 21 14 22 6 23 9 24 2 25 1 26 1 27 2 29 1 ----- ---- Total 4177 Num Instances: 4177 Num Attributes: 9 Num Continuous: 8 (Int 1 / Real 7) Num Discrete: 1 Missing values: 0 / 0.0% name type enum ints real missing distinct (1) 1 'Sex' Enum 100% 0% 0% 0 / 0% 3 / 0% 0% 2 'Length' Real 0% 0% 100% 0 / 0% 134 / 3% 0% 3 'Diameter' Real 0% 0% 100% 0 / 0% 111 / 3% 0% 4 'Height' Real 0% 0% 100% 0 / 0% 51 / 1% 0% 5 'Whole weight' Real 0% 0% 100% 0 / 0% 2429 / 58% 31% 6 'Shucked weight' Real 0% 0% 100% 0 / 0% 1515 / 36% 10% 7 'Viscera weight' Real 0% 0% 100% 0 / 0% 880 / 21% 3% 8 'Shell weight' Real 0% 0% 100% 0 / 0% 926 / 22% 8% 9 'Class_Rings' Int 0% 100% 0% 0 / 0% 28 / 1% 0%

9 features

Class_number_of_rings (target)nominal28 unique values
0 missing
Sexnominal3 unique values
0 missing
Lengthnumeric134 unique values
0 missing
Diameternumeric111 unique values
0 missing
Heightnumeric51 unique values
0 missing
Whole_weightnumeric2429 unique values
0 missing
Shucked_weightnumeric1515 unique values
0 missing
Viscera_weightnumeric880 unique values
0 missing
Shell_weightnumeric926 unique values
0 missing

107 properties

4177
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
28
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.
5.76
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
0.23
First quartile of mutual information between the nominal attributes and the target attribute.
0.75
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.11
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
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
15.5
Average number of distinct values among the attributes of the nominal type.
-0.61
First quartile of skewness among attributes of the numeric type.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.59
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
17.68
Standard deviation of the number of distinct values among attributes of the nominal type.
0.8
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.62
Mean skewness among attributes of the numeric type.
0.1
First quartile of standard deviation of attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.79
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
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.17
Mean standard deviation of attributes of the numeric type.
1.58
Second quartile (Median) of entropy among attributes.
0.75
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
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
0.8
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
16.5
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.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.6
Entropy of the target attribute values.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
689
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.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.64
Area Under the ROC Curve 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.
0.23
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.75
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.79
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
76.03
Maximum kurtosis among attributes of the numeric type.
0.23
Minimal mutual information between the nominal attributes and the target attribute.
0.59
Second quartile (Median) of skewness among attributes of the numeric type.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
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
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.55
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.
0.23
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.
0.81
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
15.39
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
28
The maximum number of distinct values among attributes of the nominal type.
0.04
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.6
Third quartile of kurtosis among attributes of the numeric type.
0.21
Average class difference between consecutive instances.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.58
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.
0.02
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.
0.59
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
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.49
Maximum standard deviation of attributes of the numeric type.
1
Number of instances belonging to the least frequent class.
22.22
Percentage of nominal attributes.
0.23
Third quartile of mutual information between the nominal attributes and the target attribute.
0.79
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
0.81
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.58
Average entropy of the attributes.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.58
First quartile of entropy among attributes.
0.72
Third quartile of skewness among attributes of the numeric type.
0.11
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
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.58
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.
0.77
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.02
First quartile of kurtosis among attributes of the numeric type.
0.22
Third quartile of standard deviation of attributes of the numeric type.
0.59
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
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.8
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.38
Mean of means among attributes of the numeric type.
0.23
Average mutual information between the nominal attributes and the target attribute.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.18
First quartile of means among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.79
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
0.81
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

100 tasks

3871 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class_number_of_rings
303 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class_number_of_rings
177 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class_number_of_rings
32 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: run_cpu_time - target_feature: Class_number_of_rings
372 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class_number_of_rings
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class_number_of_rings
1317 runs - target_feature: Class_number_of_rings
1315 runs - target_feature: Class_number_of_rings
1314 runs - target_feature: Class_number_of_rings
1314 runs - target_feature: Class_number_of_rings
1313 runs - target_feature: Class_number_of_rings
1313 runs - target_feature: Class_number_of_rings
1312 runs - target_feature: Class_number_of_rings
1312 runs - target_feature: Class_number_of_rings
1312 runs - target_feature: Class_number_of_rings
1311 runs - target_feature: Class_number_of_rings
1310 runs - target_feature: Class_number_of_rings
1310 runs - target_feature: Class_number_of_rings
1310 runs - target_feature: Class_number_of_rings
1309 runs - target_feature: Class_number_of_rings
1308 runs - target_feature: Class_number_of_rings
1307 runs - target_feature: Class_number_of_rings
1307 runs - target_feature: Class_number_of_rings
1307 runs - target_feature: Class_number_of_rings
1306 runs - target_feature: Class_number_of_rings
1304 runs - target_feature: Class_number_of_rings
1304 runs - target_feature: Class_number_of_rings
1303 runs - target_feature: Class_number_of_rings
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