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

62 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.
10
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6
The maximum number of distinct values among attributes of the nominal type.
-1.15
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.73
Third quartile of skewness among attributes of the numeric type.
6.55
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
2.36
First quartile of kurtosis among attributes of the numeric type.
1.12
Third quartile of standard deviation of attributes of the numeric type.
1.44
Maximum standard deviation of attributes of the numeric type.
4.21
Percentage of instances belonging to the least frequent class.
0.34
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
9
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
9.91
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
-0.14
First quartile of skewness among attributes of the numeric type.
11.27
Mean of means among attributes of the numeric type.
0.3
First quartile of standard deviation of attributes of the numeric type.
0.26
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.18
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3.05
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
6
Average number of distinct values among the attributes of the nominal type.
1.52
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.65
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
35.51
Percentage of instances belonging to the most frequent class.
0.69
Mean standard deviation of attributes of the numeric type.
1.63
Second quartile (Median) of skewness among attributes of the numeric type.
76
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.65
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.41
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
Third quartile of entropy among attributes.
54.69
Maximum kurtosis among attributes of the numeric type.
0.06
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
9.61
Third quartile of kurtosis among attributes of the numeric type.
72.65
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
90
Percentage of numeric attributes.
11.18
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.

11 tasks

684 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
325 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Type
321 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
169 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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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