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bridges

bridges

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Author: Yoram Reich","Steven J. Fenves Source: [original](http://openml.org/d/19) - Please cite: Pittsburgh bridges This version is derived from version 2 (the discretized version) 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

12 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
ERECTEDnominal4 unique values
0 missing
PURPOSEnominal4 unique values
0 missing
LENGTHnominal3 unique values
24 missing
LANESnominal4 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
Show all 12 features

107 properties

NumberOfInstances
105
Number of instances (rows) of the dataset.
NumberOfFeatures
12
Number of attributes (columns) of the dataset.
NumberOfClasses
6
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
61
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
35
Number of instances with at least one value missing.
NumberOfNumericFeatures
0
Number of numeric attributes.
NumberOfSymbolicFeatures
12
Number of nominal attributes.
AutoCorrelation
0.49
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.69
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
CfsSubsetEval_DecisionStumpErrRate
0.44
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
CfsSubsetEval_DecisionStumpKappa
0.3
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
CfsSubsetEval_NaiveBayesAUC
0.69
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
CfsSubsetEval_NaiveBayesErrRate
0.44
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
CfsSubsetEval_NaiveBayesKappa
0.3
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
CfsSubsetEval_kNN1NAUC
0.69
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
CfsSubsetEval_kNN1NErrRate
0.44
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
CfsSubsetEval_kNN1NKappa
0.3
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
ClassEntropy
2.32
Entropy of the target attribute values.
DecisionStumpAUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.11
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
4.82
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
41.9
Percentage of instances belonging to the most frequent class.
MajorityClassSize
44
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
5.58
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
1.56
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
54
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.69
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.48
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
2.51
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
7.67
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.59
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.17
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.52
Percentage of instances belonging to the least frequent class.
MinorityClassSize
10
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
2
Number of binary attributes.
PercentageOfBinaryFeatures
16.67
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
33.33
Percentage of instances having missing values.
PercentageOfMissingValues
4.84
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
1.07
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.24
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.49
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.36
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.58
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.6
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.62
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.62
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.62
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
14.63
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on bridges
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Classification on bridges
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Classification on bridges
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Data Stream Classification on bridges
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: TYPE
Clustering on bridges
0 runs
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
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