Data
cylinder-bands

cylinder-bands

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Author: Bob Evans, RR Donnelley & Sons Co. Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Cylinder+Bands) - August, 1995 Please cite: Cylinder bands Process delays known as cylinder banding in rotogravure printing were substantially mitigated using control rules discovered by decision tree induction. Attribute Information: > 1. timestamp: numeric;19500101 - 21001231 2. cylinder number: nominal 3. customer: nominal; 4. job number: nominal; 5. grain screened: nominal; yes, no 6. ink color: nominal; key, type 7. proof on ctd ink: nominal; yes, no 8. blade mfg: nominal; benton, daetwyler, uddeholm 9. cylinder division: nominal; gallatin, warsaw, mattoon 10. paper type: nominal; uncoated, coated, super 11. ink type: nominal; uncoated, coated, cover 12. direct steam: nominal; use; yes, no * 13. solvent type: nominal; xylol, lactol, naptha, line, other 14. type on cylinder: nominal; yes, no 15. press type: nominal; use; 70 wood hoe, 70 motter, 70 albert, 94 motter 16. press: nominal; 821, 802, 813, 824, 815, 816, 827, 828 17. unit number: nominal; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 18. cylinder size: nominal; catalog, spiegel, tabloid 19. paper mill location: nominal; north us, south us, canadian, scandanavian, mid european 20. plating tank: nominal; 1910, 1911, other 21. proof cut: numeric; 0-100 22. viscosity: numeric; 0-100 23. caliper: numeric; 0-1.0 24. ink temperature: numeric; 5-30 25. humifity: numeric; 5-120 26. roughness: numeric; 0-2 27. blade pressure: numeric; 10-75 28. varnish pct: numeric; 0-100 29. press speed: numeric; 0-4000 30. ink pct: numeric; 0-100 31. solvent pct: numeric; 0-100 32. ESA Voltage: numeric; 0-16 33. ESA Amperage: numeric; 0-10 34. wax: numeric ; 0-4.0 35. hardener: numeric; 0-3.0 36. roller durometer: numeric; 15-120 37. current density: numeric; 20-50 38. anode space ratio: numeric; 70-130 39. chrome content: numeric; 80-120 40. band type: nominal; class; band, no band Notes: * cylinder number is an identifier and should be ignored when modelling the data

38 features

band_type (target)nominal2 unique values
0 missing
viscositynumeric37 unique values
5 missing
proof_cutnumeric27 unique values
55 missing
calipernominal20 unique values
27 missing
ink_temperaturenumeric65 unique values
2 missing
humifitynumeric42 unique values
1 missing
roughnessnumeric18 unique values
30 missing
blade_pressurenumeric36 unique values
63 missing
varnish_pctnumeric122 unique values
56 missing
press_speednumeric83 unique values
10 missing
ink_pctnumeric81 unique values
56 missing
solvent_pctnumeric115 unique values
56 missing
ESA_Voltagenumeric17 unique values
57 missing
ESA_Amperagenumeric4 unique values
55 missing
waxnumeric30 unique values
6 missing
hardenernumeric29 unique values
7 missing
roller_durometernumeric12 unique values
55 missing
current_densitynominal7 unique values
7 missing
anode_space_rationumeric80 unique values
7 missing
chrome_contentnominal3 unique values
3 missing
ink_typenominal3 unique values
0 missing
cylinder_number (ignore)nominal429 unique values
0 missing
customernominal71 unique values
0 missing
job_numbernumeric262 unique values
0 missing
grain_screenednominal2 unique values
49 missing
ink_colornominal1 unique values
0 missing
proof_on_ctd_inknominal2 unique values
57 missing
blade_mfgnominal2 unique values
60 missing
cylinder_divisionnominal1 unique values
0 missing
paper_typenominal3 unique values
0 missing
timestamp (ignore)nominal296 unique values
0 missing
direct_steamnominal2 unique values
25 missing
solvent_typenominal3 unique values
55 missing
type_on_cylindernominal2 unique values
18 missing
press_typenominal4 unique values
0 missing
pressnominal8 unique values
0 missing
unit_numbernumeric7 unique values
0 missing
cylinder_sizenominal3 unique values
3 missing
paper_mill_locationnominal5 unique values
156 missing
plating_tanknominal2 unique values
18 missing

107 properties

540
Number of instances (rows) of the dataset.
38
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
999
Number of missing values in the dataset.
263
Number of instances with at least one value missing.
18
Number of numeric attributes.
20
Number of nominal attributes.
0.81
Average class difference between consecutive instances.
0.63
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
0.38
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
0.16
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
0.63
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
0.38
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
0.16
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
0.63
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
0.38
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
0.16
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
0.98
Entropy of the target attribute values.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.32
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.07
Number of attributes divided by the number of instances.
17.87
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
57.78
Percentage of instances belonging to the most frequent class.
312
Number of instances belonging to the most frequent class.
4.99
Maximum entropy among attributes.
137.58
Maximum kurtosis among attributes of the numeric type.
37287.53
Maximum of means among attributes of the numeric type.
0.22
Maximum mutual information between the nominal attributes and the target attribute.
71
The maximum number of distinct values among attributes of the nominal type.
11.53
Maximum skewness among attributes of the numeric type.
8729
Maximum standard deviation of attributes of the numeric type.
1.3
Average entropy of the attributes.
10.37
Mean kurtosis among attributes of the numeric type.
2198.88
Mean of means among attributes of the numeric type.
0.05
Average mutual information between the nominal attributes and the target attribute.
22.58
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
7.3
Average number of distinct values among the attributes of the nominal type.
1.19
Mean skewness among attributes of the numeric type.
506.96
Mean standard deviation of attributes of the numeric type.
-0
Minimal entropy among attributes.
-1.75
Minimum kurtosis among attributes of the numeric type.
0.04
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
-1.95
Minimum skewness among attributes of the numeric type.
0.19
Minimum standard deviation of attributes of the numeric type.
42.22
Percentage of instances belonging to the least frequent class.
228
Number of instances belonging to the least frequent class.
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4
Number of binary attributes.
10.53
Percentage of binary attributes.
48.7
Percentage of instances having missing values.
4.87
Percentage of missing values.
47.37
Percentage of numeric attributes.
52.63
Percentage of nominal attributes.
0.36
First quartile of entropy among attributes.
0.38
First quartile of kurtosis among attributes of the numeric type.
2.13
First quartile of means among attributes of the numeric type.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
-0.18
First quartile of skewness among attributes of the numeric type.
1.1
First quartile of standard deviation of attributes of the numeric type.
1.13
Second quartile (Median) of entropy among attributes.
1.46
Second quartile (Median) of kurtosis among attributes of the numeric type.
32.84
Second quartile (Median) of means among attributes of the numeric type.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.42
Second quartile (Median) of skewness among attributes of the numeric type.
4.76
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.39
Third quartile of entropy among attributes.
5.48
Third quartile of kurtosis among attributes of the numeric type.
61.37
Third quartile of means among attributes of the numeric type.
0.09
Third quartile of mutual information between the nominal attributes and the target attribute.
1.31
Third quartile of skewness among attributes of the numeric type.
8.3
Third quartile of standard deviation of attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
15.57
Standard deviation of the number of distinct values among attributes of the nominal type.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.32
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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