Data
cylinder-bands

cylinder-bands

active ARFF Publicly available Visibility: public Uploaded 30-03-2016 by Joaquin Vanschoren
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  • OpenML-CC18 OpenML100 study_123 study_135 study_14 study_144 study_34 study_52 study_98 study_99
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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: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html) ### Description Cylinder bands UCI dataset - Process delays known as cylinder banding in rotogravure printing were substantially mitigated using control rules discovered by decision tree induction. ### Attribute Information There are 40 attributes for 540 observations, including the class: 20 are numeric and 20 are nominal. There are missing values in 302 of the instances. ``` 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 modeling the data * data set consists of 540 observations. UCI explanation states 541, which is wrong. ### Relevant Papers Evans, B., and Fisher, D. (1994). Overcoming process delays with decision tree induction. IEEE Expert, Vol. 9, No. 1, 60--66.

40 features

band_type (target)nominal2 unique values
0 missing
timestamp (ignore)nominal296 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

62 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.
10
Percentage of binary attributes.
4.76
Second quartile (Median) of standard deviation of attributes of the numeric type.
4.99
Maximum entropy among attributes.
-1.75
Minimum kurtosis among attributes of the numeric type.
48.7
Percentage of instances having missing values.
1.39
Third quartile of entropy among attributes.
137.58
Maximum kurtosis among attributes of the numeric type.
0.04
Minimum of means among attributes of the numeric type.
4.63
Percentage of missing values.
5.48
Third quartile of kurtosis among attributes of the numeric type.
37287.53
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
45
Percentage of numeric attributes.
61.37
Third quartile of means among attributes of the numeric type.
0.22
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
55
Percentage of nominal attributes.
0.09
Third quartile of mutual information between the nominal attributes and the target attribute.
71
The maximum number of distinct values among attributes of the nominal type.
-1.95
Minimum skewness among attributes of the numeric type.
0.36
First quartile of entropy among attributes.
1.31
Third quartile of skewness among attributes of the numeric type.
11.53
Maximum skewness among attributes of the numeric type.
0.19
Minimum standard deviation of attributes of the numeric type.
0.38
First quartile of kurtosis among attributes of the numeric type.
8.3
Third quartile of standard deviation of attributes of the numeric type.
8729
Maximum standard deviation of attributes of the numeric type.
42.22
Percentage of instances belonging to the least frequent class.
2.13
First quartile of means among attributes of the numeric type.
15.57
Standard deviation of the number of distinct values among attributes of the nominal type.
1.3
Average entropy of the attributes.
228
Number of instances belonging to the least frequent class.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
10.37
Mean kurtosis among attributes of the numeric type.
4
Number of binary attributes.
-0.18
First quartile of skewness among attributes of the numeric type.
2198.88
Mean of means among attributes of the numeric type.
1.1
First quartile of standard deviation of attributes of the numeric type.
0.81
Average class difference between consecutive instances.
0.05
Average mutual information between the nominal attributes and the target attribute.
1.13
Second quartile (Median) of entropy among attributes.
0.98
Entropy of the target attribute values.
22.58
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.46
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.07
Number of attributes divided by the number of instances.
7.3
Average number of distinct values among the attributes of the nominal type.
32.84
Second quartile (Median) of means among attributes of the numeric type.
17.87
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.19
Mean skewness among attributes of the numeric type.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
57.78
Percentage of instances belonging to the most frequent class.
506.96
Mean standard deviation of attributes of the numeric type.
0.42
Second quartile (Median) of skewness among attributes of the numeric type.
312
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.

16 tasks

9046 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: band_type
4296 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: band_type
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: band_type
0 runs - estimation_procedure: 33% Holdout set - target_feature: band_type
45 runs - estimation_procedure: 10-fold Learning Curve - target_feature: band_type
0 runs - target_feature: band_type
1301 runs - target_feature: band_type
1298 runs - target_feature: band_type
0 runs - target_feature: band_type
0 runs - target_feature: band_type
0 runs - target_feature: band_type
0 runs - target_feature: band_type
0 runs - target_feature: band_type
0 runs - target_feature: band_type
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