Data
anneal

anneal

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title of Database: Annealing Data 2. Source Information: donated by David Sterling and Wray Buntine. 3. Past Usage: unknown 4. Relevant Information: -- Explanation: I suspect this was left by Ross Quinlan in 1987 at the 4th Machine Learning Workshop. I'd have to check with Jeff Schlimmer to double check this. 5. Number of Instances: 798 6. Number of Attributes: 38 -- 6 continuously-valued -- 3 integer-valued -- 29 nominal-valued 7. Attribute Information: 1. family: --,GB,GK,GS,TN,ZA,ZF,ZH,ZM,ZS 2. product-type: C, H, G 3. steel: -,R,A,U,K,M,S,W,V 4. carbon: continuous 5. hardness: continuous 6. temper_rolling: -,T 7. condition: -,S,A,X 8. formability: -,1,2,3,4,5 9. strength: continuous 10. non-ageing: -,N 11. surface-finish: P,M,- 12. surface-quality: -,D,E,F,G 13. enamelability: -,1,2,3,4,5 14. bc: Y,- 15. bf: Y,- 16. bt: Y,- 17. bw/me: B,M,- 18. bl: Y,- 19. m: Y,- 20. chrom: C,- 21. phos: P,- 22. cbond: Y,- 23. marvi: Y,- 24. exptl: Y,- 25. ferro: Y,- 26. corr: Y,- 27. blue/bright/varn/clean: B,R,V,C,- 28. lustre: Y,- 29. jurofm: Y,- 30. s: Y,- 31. p: Y,- 32. shape: COIL, SHEET 33. thick: continuous 34. width: continuous 35. len: continuous 36. oil: -,Y,N 37. bore: 0000,0500,0600,0760 38. packing: -,1,2,3 classes: 1,2,3,4,5,U -- The '-' values are actually 'not_applicable' values rather than 'missing_values' (and so can be treated as legal discrete values rather than as showing the absence of a discrete value). 8. Missing Attribute Values: Signified with "?" Attribute: Number of instances missing its value: 1 0 2 0 3 70 4 0 5 0 6 675 7 271 8 283 9 0 10 703 11 790 12 217 13 785 14 797 15 680 16 736 17 609 18 662 19 798 20 775 21 791 22 730 23 798 24 796 25 772 26 798 27 793 28 753 29 798 30 798 31 798 32 0 33 0 34 0 35 0 36 740 37 0 38 789 39 0 9. Distribution of Classes Class Name: Number of Instances: 1 8 2 88 3 608 4 0 5 60 U 34 --- 798

39 features

class (target)nominal5 unique values
0 missing
phosnominal1 unique values
891 missing
chromnominal1 unique values
872 missing
cbondnominal1 unique values
824 missing
marvinominal0 unique values
898 missing
exptlnominal1 unique values
896 missing
ferronominal1 unique values
868 missing
corrnominal0 unique values
898 missing
blue%2Fbright%2Fvarn%2Fcleannominal3 unique values
892 missing
lustrenominal1 unique values
847 missing
jurofmnominal0 unique values
898 missing
snominal0 unique values
898 missing
pnominal0 unique values
898 missing
shapenominal2 unique values
0 missing
thicknumeric50 unique values
0 missing
widthnumeric68 unique values
0 missing
lennumeric24 unique values
0 missing
oilnominal2 unique values
834 missing
borenominal3 unique values
0 missing
packingnominal2 unique values
889 missing
surface-finishnominal1 unique values
889 missing
product-typenominal1 unique values
0 missing
steelnominal7 unique values
86 missing
carbonnumeric10 unique values
0 missing
hardnessnumeric7 unique values
0 missing
temper_rollingnominal1 unique values
761 missing
conditionnominal2 unique values
303 missing
formabilitynominal4 unique values
318 missing
strengthnumeric8 unique values
0 missing
non-ageingnominal1 unique values
793 missing
familynominal2 unique values
772 missing
surface-qualitynominal4 unique values
244 missing
enamelabilitynominal2 unique values
882 missing
bcnominal1 unique values
897 missing
bfnominal1 unique values
769 missing
btnominal1 unique values
824 missing
bw%2Fmenominal2 unique values
687 missing
blnominal1 unique values
749 missing
mnominal0 unique values
898 missing

107 properties

898
Number of instances (rows) of the dataset.
39
Number of attributes (columns) of the dataset.
5
Number of distinct values of the target attribute (if it is nominal).
22175
Number of missing values in the dataset.
898
Number of instances with at least one value missing.
6
Number of numeric attributes.
33
Number of nominal attributes.
0.61
Average class difference between consecutive instances.
0.91
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
0.13
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
0.62
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
0.91
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
0.13
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
0.62
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
0.91
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
0.13
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
0.62
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
1.19
Entropy of the target attribute values.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.23
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.04
Number of attributes divided by the number of instances.
26.84
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
76.17
Percentage of instances belonging to the most frequent class.
684
Number of instances belonging to the most frequent class.
1.82
Maximum entropy among attributes.
13.22
Maximum kurtosis among attributes of the numeric type.
1263.09
Maximum of means among attributes of the numeric type.
0.41
Maximum mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
3.76
Maximum skewness among attributes of the numeric type.
1871.4
Maximum standard deviation of attributes of the numeric type.
0.25
Average entropy of the attributes.
4.65
Mean kurtosis among attributes of the numeric type.
348.5
Mean of means among attributes of the numeric type.
0.04
Average mutual information between the nominal attributes and the target attribute.
4.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.64
Average number of distinct values among the attributes of the nominal type.
2.03
Mean skewness among attributes of the numeric type.
405.17
Mean standard deviation of attributes of the numeric type.
-0
Minimal entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
1.2
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
The minimal number of distinct values among attributes of the nominal type.
0.07
Minimum skewness among attributes of the numeric type.
0.87
Minimum standard deviation of attributes of the numeric type.
0.89
Percentage of instances belonging to the least frequent class.
8
Number of instances belonging to the least frequent class.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4
Number of binary attributes.
10.26
Percentage of binary attributes.
100
Percentage of instances having missing values.
63.32
Percentage of missing values.
15.38
Percentage of numeric attributes.
84.62
Percentage of nominal attributes.
0
First quartile of entropy among attributes.
-0.4
First quartile of kurtosis among attributes of the numeric type.
3.03
First quartile of means among attributes of the numeric type.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.97
First quartile of skewness among attributes of the numeric type.
10.51
First quartile of standard deviation of attributes of the numeric type.
0
Second quartile (Median) of entropy among attributes.
1.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
21.22
Second quartile (Median) of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1.65
Second quartile (Median) of skewness among attributes of the numeric type.
69.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.24
Third quartile of entropy among attributes.
12.74
Third quartile of kurtosis among attributes of the numeric type.
901.26
Third quartile of means among attributes of the numeric type.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
3.75
Third quartile of skewness among attributes of the numeric type.
771.86
Third quartile of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1.56
Standard deviation of the number of distinct values among attributes of the nominal type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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