Data
anneal

anneal

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
0 likes downloaded by 15 people , 18 total downloads 0 issues 0 downvotes
  • study_1 study_14 study_34 study_37 study_41 study_70 study_76 uci
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Unknown. Donated by David Sterling and Wray Buntine Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Annealing) - 1990 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) The original Annealing dataset from UCI. The exact meaning of the features and classes is largely unknown. Annealing, in metallurgy and materials science, is a heat treatment that alters the physical and sometimes chemical properties of a material to increase its ductility and reduce its hardness, making it more workable. It involves heating a material to above its recrystallization temperature, maintaining a suitable temperature, and then cooling. (Wikipedia) ### 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).

39 features

class (target)nominal5 unique values
0 missing
familynominal2 unique values
772 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
surface-finishnominal1 unique values
889 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
chromnominal1 unique values
872 missing
phosnominal1 unique values
891 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

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.
1.56
Standard deviation of the number of distinct values among attributes of the nominal type.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.64
Average number of distinct values among the attributes of the nominal type.
0.97
First quartile of skewness among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.7
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.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.06
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
Second quartile (Median) of entropy among attributes.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.83
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.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.19
Entropy of the target attribute values.
1.82
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.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Area Under the ROC Curve 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
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.23
Error rate 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.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.41
Maximum mutual information between the nominal attributes and the target attribute.
0
The minimal number of distinct values among attributes of the nominal type.
10.26
Percentage of binary attributes.
69.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.93
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.
7
The maximum number of distinct values among attributes of the nominal type.
0.07
Minimum skewness among attributes of the numeric type.
100
Percentage of instances having missing values.
0.24
Third quartile of entropy among attributes.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
26.84
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.76
Maximum skewness among attributes of the numeric type.
0.87
Minimum standard deviation of attributes of the numeric type.
63.32
Percentage of missing values.
12.74
Third quartile of kurtosis among attributes of the numeric type.
0.61
Average class difference between consecutive instances.
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.94
Area Under the ROC Curve 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.
0.91
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
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
0.25
Average entropy of the attributes.
8
Number of instances belonging to the least frequent class.
84.62
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.94
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.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of entropy among attributes.
3.75
Third quartile of skewness among attributes of the numeric type.
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
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
348.5
Mean of means among attributes of the numeric type.
0.25
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.
0.91
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.04
Average mutual information between the nominal attributes and the target attribute.
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.03
First quartile of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.08
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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

36 tasks

5741 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
315 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
297 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
192 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
181 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class - cost matrix: [[0,1,2]]
31 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
316 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
143 runs - estimation_procedure: 10 times 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
62 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
1302 runs - target_feature: class
1299 runs - target_feature: class
1298 runs - target_feature: class
1297 runs - target_feature: class
1297 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
Define a new task