Data
anneal

anneal

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Neeratyoy Mallik
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Default target attribute(s) could not be found: [y],Default target attribute(s) could not be found: [y]
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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

19 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
Percentage of instances having missing values.
0.61
Average class difference between consecutive instances.
0
Percentage of missing values.
0.04
Number of attributes divided by the number of instances.
15.38
Percentage of numeric attributes.
76.17
Percentage of instances belonging to the most frequent class.
84.62
Percentage of nominal attributes.
684
Number of instances belonging to the most frequent class.
0.89
Percentage of instances belonging to the least frequent class.
8
Number of instances belonging to the least frequent class.
19
Number of binary attributes.
48.72
Percentage of binary attributes.

2 tasks

0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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