Data
anneal

anneal

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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
Show all 39 features

107 properties

NumberOfInstances
898
Number of instances (rows) of the dataset.
NumberOfFeatures
39
Number of attributes (columns) of the dataset.
NumberOfClasses
5
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
22175
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
898
Number of instances with at least one value missing.
NumberOfNumericFeatures
6
Number of numeric attributes.
NumberOfSymbolicFeatures
33
Number of nominal attributes.
AutoCorrelation
0.61
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
1.19
Entropy of the target attribute values.
DecisionStumpAUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.04
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
26.84
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
76.17
Percentage of instances belonging to the most frequent class.
MajorityClassSize
684
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1.82
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
13.22
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1263.09
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.41
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
7
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
3.76
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1871.4
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.25
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
4.65
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
348.5
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.04
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
4.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
1.64
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
2.03
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
405.17
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
-0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.97
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
1.2
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
0
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.07
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.87
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.89
Percentage of instances belonging to the least frequent class.
MinorityClassSize
8
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
10.26
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
100
Percentage of instances having missing values.
PercentageOfMissingValues
63.32
Percentage of missing values.
PercentageOfNumericFeatures
15.38
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
84.62
Percentage of nominal attributes.
Quartile1AttributeEntropy
0
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.4
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
3.03
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.97
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
10.51
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
1.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
21.22
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.65
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
69.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.24
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
12.74
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
901.26
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
3.75
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
771.86
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
1.56
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

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