Data
cylinder-bands

cylinder-bands

deactivated ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
0 likes downloaded by 4 people , 5 total downloads 0 issues 0 downvotes
  • mythbusting_1 study_1 study_15 study_20 study_37 study_70 uci
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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

40 features

band_type (target)nominal2 unique values
0 missing
timestampnominal296 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
ink_typenominal3 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
proof_cutnumeric27 unique values
55 missing
viscositynumeric37 unique values
5 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

119 properties

540
Number of instances (rows) of the dataset.
40
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.
22
Number of nominal attributes.
16.98
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2.4
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.32
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
7.95
Maximum entropy among attributes.
3.22
Mean skewness among attributes of the numeric type.
4
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.81
Average class difference between consecutive instances.
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
156.93
Mean standard deviation of attributes of the numeric type.
0.02
First quartile of skewness among attributes of the numeric type.
4058916576
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.58
The predictive accuracy obtained by always predicting the majority class.
0
Minimal entropy among attributes.
2.35
DataQuality extracted from Fantail Library
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.07
Number of attributes divided by the number of instances.
4811.61
Maximum kurtosis among attributes of the numeric type.
1.15
Second quartile (Median) of entropy among attributes.
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
10.84
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
66.45
Standard deviation of the number of distinct values among attributes of the nominal type.
37287.53
Maximum of means among attributes of the numeric type.
-1.75
Minimum kurtosis among attributes of the numeric type.
0.68
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.5
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.77
Maximum mutual information between the nominal attributes and the target attribute.
0
Minimum of means among attributes of the numeric type.
10.05
Second quartile (Median) of means among attributes of the numeric type.
0.42
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
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.42
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
297
The maximum number of distinct values among attributes of the nominal type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
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
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
67.4
Maximum skewness among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0.76
Second quartile (Median) of skewness among attributes of the numeric type.
0.5
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
8729
DataQuality extracted from Fantail Library
-1.94
Minimum skewness among attributes of the numeric type.
10
Percentage of binary attributes.
5.94
DataQuality extracted from Fantail Library
0.42
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.42
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.63
Average entropy of the attributes.
0
DataQuality extracted from Fantail Library
48.7
Percentage of instances having missing values.
1.67
Third quartile of entropy among attributes.
0
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.42
Percentage of instances belonging to the least frequent class.
4.63
Percentage of missing values.
3.21
Third quartile of kurtosis among attributes of the numeric type.
0.5
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
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
228
Number of instances belonging to the least frequent class.
45
Percentage of numeric attributes.
54.72
Third quartile of means among attributes of the numeric type.
0.42
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.42
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
96.07
Mean kurtosis among attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
55
Percentage of nominal attributes.
0.1
Third quartile of mutual information between the nominal attributes and the target attribute.
0
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
288.02
Mean of means among attributes of the numeric type.
0.31
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.4
First quartile of entropy among attributes.
1.52
DataQuality extracted from Fantail Library
0.98
Entropy of the target attribute values.
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
57.78
Percentage of instances belonging to the most frequent class.
0.09
Average mutual information between the nominal attributes and the target attribute.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.76
First quartile of kurtosis among attributes of the numeric type.
30.07
DataQuality extracted from Fantail Library
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
312
Number of instances belonging to the most frequent class.
22.6
Average number of distinct values among the attributes of the nominal type.

9 tasks

787 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: band_type
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: band_type
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: band_type
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: band_type
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: band_type
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: band_type
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: band_type
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: band_type
0 runs - estimation_procedure: 50 times Clustering
Define a new task

Discussions

Loading discussions...