Data
bridges

bridges

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Author: Yoram Reich","Steven J. Fenves Source: [original](http://openml.org/d/17) - Please cite: Pittsburgh bridges This version is derived from version 1 by removing all instances with missing values in the last (target) attribute. The bridges dataset is originally not a classification dataset, put is used so extensively in the literature, using the last attribute as the target attribute. However, this attribute has missing values, which may lead to confusing benchmarking result. Therefore, these instances have been removed. Sources: -- Yoram Reich and Steven J. Fenves Department of Civil Engineering and Engineering Design Research Center Carnegie Mellon University Pittsburgh, PA 15213 Compiled from various sources. -- Date: 1 August 1990 Attribute Information: The type field state whether a property is continuous/integer (c) or nominal (n). For properties with c,n type, the range of continuous numbers is given first and the possible values of the nominal follow the semi-colon. name type possible values comments ------------------------------------------------------------------------ 1. IDENTIF - - identifier of the examples 2. RIVER n A, M, O 3. LOCATION n 1 to 52 4. ERECTED c,n 1818-1986 - CRAFTS, EMERGING, MATURE, MODERN 5. PURPOSE n WALK, AQUEDUCT, RR, HIGHWAY 6. LENGTH c,n 804-4558 - SHORT, MEDIUM, LONG 7. LANES c,n 1, 2, 4, 6 - 1, 2, 4, 6 8. CLEAR-G n N, G 9. T-OR-D n THROUGH, DECK 10. MATERIAL n WOOD, IRON, STEEL 11. SPAN n SHORT, MEDIUM, LONG 12. REL-L n S, S-F, F 13. TYPE n WOOD, SUSPEN, SIMPLE-T, ARCH, CANTILEV, CONT-T

13 features

TYPE (target)nominal6 unique values
0 missing
IDENTIF (row identifier)nominal105 unique values
0 missing
RIVERnominal4 unique values
0 missing
LOCATIONnominal54 unique values
1 missing
ERECTEDnumeric68 unique values
0 missing
PURPOSEnominal4 unique values
0 missing
LENGTHnumeric64 unique values
24 missing
LANESnumeric4 unique values
13 missing
CLEAR-Gnominal2 unique values
2 missing
T-OR-Dnominal2 unique values
3 missing
MATERIALnominal3 unique values
0 missing
SPANnominal3 unique values
13 missing
REL-Lnominal3 unique values
5 missing

107 properties

105
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
6
Number of distinct values of the target attribute (if it is nominal).
61
Number of missing values in the dataset.
35
Number of instances with at least one value missing.
3
Number of numeric attributes.
10
Number of nominal attributes.
2.32
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0.26
First quartile of mutual information between the nominal attributes and the target attribute.
0.62
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.38
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
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
9
Average number of distinct values among the attributes of the nominal type.
-0.2
First quartile of skewness among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
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
16.92
Standard deviation of the number of distinct values among attributes of the nominal type.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.94
Mean skewness among attributes of the numeric type.
1.16
First quartile of standard deviation of attributes of the numeric type.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.43
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
261.57
Mean standard deviation of attributes of the numeric type.
1.29
Second quartile (Median) of entropy among attributes.
0.62
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.38
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
0.39
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
41.9
Percentage of instances belonging to the most frequent class.
0.59
Minimal entropy among attributes.
0.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.32
Entropy of the target attribute values.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
44
Number of instances belonging to the most frequent class.
-0.4
Minimum kurtosis among attributes of the numeric type.
1567.47
Second quartile (Median) of means among attributes of the numeric type.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
5.58
Maximum entropy among attributes.
2.63
Minimum of means among attributes of the numeric type.
0.36
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.62
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.43
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
3.58
Maximum kurtosis among attributes of the numeric type.
0.17
Minimal mutual information between the nominal attributes and the target attribute.
1.28
Second quartile (Median) of skewness among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1903.73
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
15.38
Percentage of binary attributes.
36.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.12
Number of attributes divided by the number of instances.
1.56
Maximum mutual information between the nominal attributes and the target attribute.
-0.2
Minimum skewness among attributes of the numeric type.
33.33
Percentage of instances having missing values.
1.56
Third quartile of entropy among attributes.
0.59
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
4.48
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
54
The maximum number of distinct values among attributes of the nominal type.
1.16
Minimum standard deviation of attributes of the numeric type.
4.47
Percentage of missing values.
3.58
Third quartile of kurtosis among attributes of the numeric type.
0.49
Average class difference between consecutive instances.
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.74
Maximum skewness among attributes of the numeric type.
9.52
Percentage of instances belonging to the least frequent class.
23.08
Percentage of numeric attributes.
1903.73
Third quartile of means among attributes of the numeric type.
0.77
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
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
747.49
Maximum standard deviation of attributes of the numeric type.
10
Number of instances belonging to the least frequent class.
76.92
Percentage of nominal attributes.
0.63
Third quartile of mutual information between the nominal attributes and the target attribute.
0.43
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
0.59
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.72
Average entropy of the attributes.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.9
First quartile of entropy among attributes.
1.74
Third quartile of skewness among attributes of the numeric type.
0.38
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
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.37
Mean kurtosis among attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.4
First quartile of kurtosis among attributes of the numeric type.
747.49
Third quartile of standard deviation of attributes of the numeric type.
0.77
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
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1157.94
Mean of means among attributes of the numeric type.
0.52
Average mutual information between the nominal attributes and the target attribute.
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.63
First quartile of means among attributes of the numeric type.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.43
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
0.59
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

6 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: TYPE
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: TYPE
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