Data
anneal

anneal

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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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

62 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.
0.07
Minimum skewness among attributes of the numeric type.
84.62
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
0.87
Minimum standard deviation of attributes of the numeric type.
0
First quartile of entropy among attributes.
3.75
Third quartile of skewness among attributes of the numeric type.
3.76
Maximum skewness among attributes of the numeric type.
0.89
Percentage of instances belonging to the least frequent class.
-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.
1871.4
Maximum standard deviation of attributes of the numeric type.
8
Number of instances belonging to the least frequent class.
3.03
First quartile of means among attributes of the numeric type.
1.56
Standard deviation of the number of distinct values among attributes of the nominal type.
0.25
Average entropy of the attributes.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
4.65
Mean kurtosis among attributes of the numeric type.
0.97
First quartile of skewness among attributes of the numeric type.
348.5
Mean of means among attributes of the numeric type.
10.51
First quartile of standard deviation of attributes of the numeric type.
0.61
Average class difference between consecutive instances.
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.
0
Second quartile (Median) of entropy among attributes.
1.19
Entropy of the target attribute values.
1.64
Average number of distinct values among the attributes of the nominal type.
1.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.04
Number of attributes divided by the number of instances.
2.03
Mean skewness among attributes of the numeric type.
21.22
Second quartile (Median) of means among attributes of the numeric type.
26.84
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
405.17
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
76.17
Percentage of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
1.65
Second quartile (Median) of skewness among attributes of the numeric type.
684
Number of instances belonging to the most frequent class.
-0.97
Minimum kurtosis among attributes of the numeric type.
10.26
Percentage of binary attributes.
69.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.82
Maximum entropy among attributes.
1.2
Minimum of means among attributes of the numeric type.
100
Percentage of instances having missing values.
0.24
Third quartile of entropy among attributes.
13.22
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
63.32
Percentage of missing values.
12.74
Third quartile of kurtosis among attributes of the numeric type.
1263.09
Maximum of means among attributes of the numeric type.
0
The minimal number of distinct values among attributes of the nominal type.
15.38
Percentage of numeric attributes.
901.26
Third quartile of means among attributes of the numeric type.
0.41
Maximum mutual information between the nominal attributes and the target attribute.

40 tasks

5737 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
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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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
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