Data
glass

glass

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title: Glass Identification Database 2. Sources: (a) Creator: B. German -- Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire RG7 4PN (b) Donor: Vina Spiehler, Ph.D., DABFT Diagnostic Products Corporation (213) 776-0180 (ext 3014) (c) Date: September, 1987 3. Past Usage: -- Rule Induction in Forensic Science -- Ian W. Evett and Ernest J. Spiehler -- Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire RG7 4PN -- Unknown technical note number (sorry, not listed here) -- General Results: nearest neighbor held its own with respect to the rule-based system 4. Relevant Information:n Vina conducted a comparison test of her rule-based system, BEAGLE, the nearest-neighbor algorithm, and discriminant analysis. BEAGLE is a product available through VRS Consulting, Inc.; 4676 Admiralty Way, Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189. In determining whether the glass was a type of "float" glass or not, the following results were obtained (# incorrect answers): Type of Sample Beagle NN DA Windows that were float processed (87) 10 12 21 Windows that were not: (76) 19 16 22 The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence...if it is correctly identified! 5. Number of Instances: 214 6. Number of Attributes: 10 (including an Id#) plus the class attribute -- all attributes are continuously valued 7. Attribute Information: 1. Id number: 1 to 214 2. RI: refractive index 3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10) 4. Mg: Magnesium 5. Al: Aluminum 6. Si: Silicon 7. K: Potassium 8. Ca: Calcium 9. Ba: Barium 10. Fe: Iron 11. Type of glass: (class attribute) -- 1 building_windows_float_processed -- 2 building_windows_non_float_processed -- 3 vehicle_windows_float_processed -- 4 vehicle_windows_non_float_processed (none in this database) -- 5 containers -- 6 tableware -- 7 headlamps 8. Missing Attribute Values: None Summary Statistics: Attribute: Min Max Mean SD Correlation with class 2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642 3. Na: 10.73 17.38 13.4079 0.8166 0.5030 4. Mg: 0 4.49 2.6845 1.4424 -0.7447 5. Al: 0.29 3.5 1.4449 0.4993 0.5988 6. Si: 69.81 75.41 72.6509 0.7745 0.1515 7. K: 0 6.21 0.4971 0.6522 -0.0100 8. Ca: 5.43 16.19 8.9570 1.4232 0.0007 9. Ba: 0 3.15 0.1750 0.4972 0.5751 10. Fe: 0 0.51 0.0570 0.0974 -0.1879 9. Class Distribution: (out of 214 total instances) -- 163 Window glass (building windows and vehicle windows) -- 87 float processed -- 70 building windows -- 17 vehicle windows -- 76 non-float processed -- 76 building windows -- 0 vehicle windows -- 51 Non-window glass -- 13 containers -- 9 tableware -- 29 headlamps Relabeled values in attribute 'Type' From: '1' To: 'build wind float' From: '2' To: 'build wind non-float' From: '3' To: 'vehic wind float' From: '4' To: 'vehic wind non-float' From: '5' To: containers From: '6' To: tableware From: '7' To: headlamps

10 features

Type (target)nominal6 unique values
0 missing
RInumeric178 unique values
0 missing
Nanumeric142 unique values
0 missing
Mgnumeric94 unique values
0 missing
Alnumeric118 unique values
0 missing
Sinumeric133 unique values
0 missing
Knumeric65 unique values
0 missing
Canumeric143 unique values
0 missing
Banumeric34 unique values
0 missing
Fenumeric32 unique values
0 missing

19 properties

214
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
6
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.
9
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.26
Average class difference between consecutive instances.
0
Percentage of missing values.
0.05
Number of attributes divided by the number of instances.
90
Percentage of numeric attributes.
35.51
Percentage of instances belonging to the most frequent class.
10
Percentage of nominal attributes.
76
Number of instances belonging to the most frequent class.
4.21
Percentage of instances belonging to the least frequent class.
9
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

10 tasks

697 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
326 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Type
322 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
170 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Type
175 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Type
78 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Type
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: Type
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