Data
anneal

anneal

deactivated ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: donated by David Sterling and Wray Buntine Source: [original (UCI)](http://www.openml.org/d/2) - Please cite: This is a preprocessed version of the anneal dataset (version 1). All missing values are treated as a nominal value with label '?'. (Quotes for clarity). Because this is not good practice, this dataset is deactivated. Use version 1 instead 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: 898 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
familynominal3 unique values
0 missing
product-typenominal1 unique values
0 missing
steelnominal8 unique values
0 missing
carbonnumeric10 unique values
0 missing
hardnessnumeric7 unique values
0 missing
temper_rollingnominal2 unique values
0 missing
conditionnominal3 unique values
0 missing
formabilitynominal5 unique values
0 missing
strengthnumeric8 unique values
0 missing
non-ageingnominal2 unique values
0 missing
surface-finishnominal2 unique values
0 missing
surface-qualitynominal5 unique values
0 missing
enamelabilitynominal3 unique values
0 missing
bcnominal2 unique values
0 missing
bfnominal2 unique values
0 missing
btnominal2 unique values
0 missing
bw%2Fmenominal3 unique values
0 missing
blnominal2 unique values
0 missing
mnominal1 unique values
0 missing
chromnominal2 unique values
0 missing
phosnominal2 unique values
0 missing
cbondnominal2 unique values
0 missing
marvinominal1 unique values
0 missing
exptlnominal2 unique values
0 missing
ferronominal2 unique values
0 missing
corrnominal1 unique values
0 missing
blue%2Fbright%2Fvarn%2Fcleannominal4 unique values
0 missing
lustrenominal2 unique values
0 missing
jurofmnominal1 unique values
0 missing
snominal1 unique values
0 missing
pnominal1 unique values
0 missing
shapenominal2 unique values
0 missing
thicknumeric50 unique values
0 missing
widthnumeric68 unique values
0 missing
lennumeric24 unique values
0 missing
oilnominal3 unique values
0 missing
borenominal3 unique values
0 missing
packingnominal3 unique values
0 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).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
6
Number of numeric attributes.
33
Number of nominal attributes.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4.65
Mean kurtosis among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.02
First quartile of entropy among attributes.
3.75
Third quartile of skewness among attributes of the numeric type.
0.94
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
348.5
Mean of means among attributes of the numeric type.
0.14
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.4
First quartile of kurtosis among attributes of the numeric type.
771.86
Third quartile of standard deviation of attributes of the numeric type.
0.98
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
0.02
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.08
Average mutual information between the nominal attributes and the target attribute.
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.03
First quartile of means among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.02
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
0.94
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.66
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
19
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
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
1.5
Standard deviation of the number of distinct values among attributes of the nominal type.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.52
Average number of distinct values among the attributes of the nominal type.
0.97
First quartile of skewness among attributes of the numeric type.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.02
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
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.03
Mean skewness among attributes of the numeric type.
10.51
First quartile of standard deviation of attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.94
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
0.03
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
76.17
Percentage of instances belonging to the most frequent class.
405.17
Mean standard deviation of attributes of the numeric type.
0.26
Second quartile (Median) of entropy among attributes.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.19
Entropy of the target attribute values.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
684
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
1.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.05
Maximum entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
21.22
Second quartile (Median) of means among attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.23
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
13.22
Maximum kurtosis among attributes of the numeric type.
1.2
Minimum of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1263.09
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1.65
Second quartile (Median) of skewness among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
0.44
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
48.72
Percentage of binary attributes.
69.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
14.54
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
8
The maximum number of distinct values among attributes of the nominal type.
0.07
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.64
Third quartile of entropy among attributes.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.76
Maximum skewness among attributes of the numeric type.
0.87
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
12.74
Third quartile of kurtosis among attributes of the numeric type.
0.61
Average class difference between consecutive instances.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1871.4
Maximum standard deviation of attributes of the numeric type.
0.89
Percentage of instances belonging to the least frequent class.
15.38
Percentage of numeric attributes.
901.26
Third quartile of means among attributes of the numeric type.
0.98
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
0.02
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.46
Average entropy of the attributes.
8
Number of instances belonging to the least frequent class.
84.62
Percentage of nominal attributes.
0.13
Third quartile of mutual information between the nominal attributes and the target attribute.
0.02
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

18 tasks

935 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
300 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
298 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
185 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
24 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
181 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
79 runs - estimation_procedure: 10-fold Learning Curve - 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
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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