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anneal

anneal

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  • study_14 test_lazy_tag_107378 test_lazy_tag_120303 test_lazy_tag_131210 test_lazy_tag_254767 test_lazy_tag_38672 test_lazy_tag_452011 test_lazy_tag_45600 test_lazy_tag_574410 test_lazy_tag_624164 test_lazy_tag_834703 test_lazy_tag_898124 test_lazy_tag_959534 test_tag_659539 study_1 study_170 study_2 study_5 study_8 study_11 study_17 study_20 study_23 study_26 study_29 study_32 study_36 study_39 study_41 study_44 study_48 study_50 study_53 study_56 study_59 study_62 study_66 study_69 study_71 study_74 study_81 study_87 study_92 study_96 study_126 study_132 study_135 study_144 study_149 study_152 study_155 study_156 study_160 study_165 study_174 study_181 study_187 study_193 study_200 study_204 study_208 study_219 study_223 study_226 study_228 study_231 study_234 study_240 study_244 study_254 study_258 study_268 study_270 study_281 study_287 study_298 study_302 study_309 study_315 study_319 study_322 study_327 study_338 study_343 study_348 study_355 study_357 study_361 study_371 study_373 study_385 study_389 study_397 study_402 study_415 study_416 study_420 study_424 study_434 study_439 study_443 study_448 study_449 study_461 study_474 study_476 study_479 study_482 study_492 study_493 study_502 study_506 study_511 study_516 study_523 study_532 study_536 study_544 study_549 study_553 study_558 study_562 study_567 study_574 study_581 study_589 study_594 study_599 study_607 study_614 study_621 study_629 study_635 study_644 study_650 study_653 study_658 study_660 study_667 study_672 study_681 study_683 study_690 study_693 study_702 study_707 study_711 study_720 study_725 study_733 study_735 study_743 study_751 study_754 study_762 study_767 study_774 study_779 study_786 study_790 study_797 study_800 study_2 study_5 study_8 study_11 study_17 study_20 study_23 study_26 study_29 study_32 study_36 study_39 study_41 study_44 study_48 study_50 study_53 study_56 study_59 study_62 study_66 study_69 study_71 study_74 study_81 study_87 study_92 study_96 study_126 study_132 study_135 study_144 study_149 study_152 study_155 study_156 study_160 study_165 study_174 study_181 study_187 study_193 study_200 study_204 study_208 study_219 study_223 study_226 study_228 study_231 study_234 study_240 study_244 study_254 study_258 study_268 study_270 study_281 study_287 study_298 study_302 study_309 study_315 study_319 study_322 study_327 study_338 study_343 study_348 study_355 study_357 study_361 study_371 study_373 study_385 study_389 study_397 study_402 study_415 study_416 study_420 study_424 study_434 study_439 study_443 study_448 study_449 study_461 study_474 study_476 study_479 study_480 study_482 study_492 study_493 study_502 study_506 study_511 study_516 study_523 study_532 study_536 study_544 study_549 study_553 study_558 study_562 study_567 study_574 study_581 study_589 study_594 study_599 study_607 study_614 study_621 study_629 study_635 study_644 study_650 study_653 study_658 study_660 study_667 study_672 study_681 study_683 study_690 study_693 study_702 study_707 study_711 study_720 study_725 study_733 study_735 study_743 study_751 study_754 study_762 study_767 study_774 study_779 study_786 study_790 study_797 study_800 study_2 study_5 study_8 study_11 study_17 study_20 study_23 study_26 study_29 study_32 study_36 study_39 study_41 study_44 study_48 study_50 study_53 study_56 study_59 study_62 study_66 study_69 study_71 study_74 study_81 study_87 study_92 study_96 study_126 study_132 study_135 study_144 study_149 study_152 study_155 study_156 study_160 study_165 study_174 study_181 study_187 study_193 study_200 study_204 study_208 study_219 study_223 study_226 study_228 study_231 study_234 study_240 study_244 study_254 study_258 study_268 study_270 study_281 study_287 study_298 study_302 study_309 study_315 study_319 study_322 study_327 study_338 study_343 study_348 study_355 study_357 study_361 study_371 study_373 study_385 study_389 study_397 study_402 study_415 study_416 study_420 study_424 study_434 study_439 study_443 study_448 study_449 study_461 study_474 study_476 study_479 study_482 study_492 study_493 study_502 study_506 study_511 study_516 study_523 study_532 study_536 study_544 study_549 study_553 study_558 study_562 study_567 study_574 study_581 study_589 study_594 study_599 study_607 study_614 study_621 study_629 study_635 study_644 study_650 study_653 study_658 study_660 study_667 study_672 study_681 study_683 study_690 study_693 study_702 study_707 study_711 study_720 study_725 study_733 study_735 study_743 study_751 study_754 study_762 study_767 study_774 study_779 study_786 study_790 study_797 study_800 study_110 study_118 study_393 study_378 study_446 study_213 study_79 study_102 study_284 study_366 study_139 study_169 study_188 study_195 study_460 study_491 study_529 study_537 study_732 study_804
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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

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.
0.61
Average class difference between consecutive instances.
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
0.13
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
0.62
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
0.91
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
0.13
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
0.62
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
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
0.13
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
0.62
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
1.19
Entropy of the target attribute values.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.23
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.04
Number of attributes divided by the number of instances.
26.84
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
76.17
Percentage of instances belonging to the most frequent class.
684
Number of instances belonging to the most frequent class.
1.82
Maximum entropy among attributes.
13.22
Maximum kurtosis among attributes of the numeric type.
1263.09
Maximum of means among attributes of the numeric type.
0.41
Maximum mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
3.76
Maximum skewness among attributes of the numeric type.
1871.4
Maximum standard deviation of attributes of the numeric type.
0.25
Average entropy of the attributes.
4.65
Mean kurtosis among attributes of the numeric type.
348.5
Mean of means among attributes of the numeric type.
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.
1.64
Average number of distinct values among the attributes of the nominal type.
2.03
Mean skewness among attributes of the numeric type.
405.17
Mean standard deviation of attributes of the numeric type.
-0
Minimal entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
1.2
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
The minimal number of distinct values among attributes of the nominal type.
0.07
Minimum skewness among attributes of the numeric type.
0.87
Minimum standard deviation of attributes of the numeric type.
0.89
Percentage of instances belonging to the least frequent class.
8
Number of instances belonging to the least frequent class.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4
Number of binary attributes.
10.26
Percentage of binary attributes.
100
Percentage of instances having missing values.
63.32
Percentage of missing values.
15.38
Percentage of numeric attributes.
84.62
Percentage of nominal attributes.
0
First quartile of entropy among attributes.
-0.4
First quartile of kurtosis among attributes of the numeric type.
3.03
First quartile of means among attributes of the numeric type.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.97
First quartile of skewness among attributes of the numeric type.
10.51
First quartile of standard deviation of attributes of the numeric type.
0
Second quartile (Median) of entropy among attributes.
1.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
21.22
Second quartile (Median) of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1.65
Second quartile (Median) of skewness among attributes of the numeric type.
69.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.24
Third quartile of entropy among attributes.
12.74
Third quartile of kurtosis among attributes of the numeric type.
901.26
Third quartile of means among attributes of the numeric type.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
3.75
Third quartile of skewness among attributes of the numeric type.
771.86
Third 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 1
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
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
1.56
Standard deviation of the number of distinct values among attributes of the nominal type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

60 runs - estimation_procedure: 33% Holdout set - target_feature: class
12 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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