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BNG(autos,10000,10)

BNG(autos,10000,10)

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26 features

symboling (target)nominal7 unique values
0 missing
engine-typenominal7 unique values
0 missing
pricenumeric999966 unique values
0 missing
highway-mpgnumeric975948 unique values
0 missing
city-mpgnumeric972354 unique values
0 missing
peak-rpmnumeric999472 unique values
0 missing
horsepowernumeric994727 unique values
0 missing
compression-rationumeric680057 unique values
0 missing
strokenumeric609531 unique values
0 missing
borenumeric621996 unique values
0 missing
fuel-systemnominal8 unique values
0 missing
engine-sizenumeric994373 unique values
0 missing
num-of-cylindersnominal7 unique values
0 missing
normalized-lossesnumeric994619 unique values
0 missing
curb-weightnumeric999699 unique values
0 missing
heightnumeric942920 unique values
0 missing
widthnumeric925045 unique values
0 missing
lengthnumeric986174 unique values
0 missing
wheel-basenumeric966587 unique values
0 missing
engine-locationnominal2 unique values
0 missing
drive-wheelsnominal3 unique values
0 missing
body-stylenominal5 unique values
0 missing
num-of-doorsnominal2 unique values
0 missing
aspirationnominal2 unique values
0 missing
fuel-typenominal2 unique values
0 missing
makenominal22 unique values
0 missing
Show all 26 features

107 properties

NumberOfInstances
1000000
Number of instances (rows) of the dataset.
NumberOfFeatures
26
Number of attributes (columns) of the dataset.
NumberOfClasses
7
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
15
Number of numeric attributes.
NumberOfSymbolicFeatures
11
Number of nominal attributes.
AutoCorrelation
0.23
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.7
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
0.48
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
0.37
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
0.7
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
0.48
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
0.37
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
0.7
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
0.48
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
0.37
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
2.3
Entropy of the target attribute values.
DecisionStumpAUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.59
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
41.46
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
32.36
Percentage of instances belonging to the most frequent class.
MajorityClassSize
323554
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.42
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
7.45
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
13245.34
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.12
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
22
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
2.56
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
7832.23
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.89
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.89
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
1450.08
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.06
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
33.02
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
6.09
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.76
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
603.23
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.51
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.98
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
3.25
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.01
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
-0.59
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.27
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.24
Percentage of instances belonging to the least frequent class.
MinorityClassSize
2441
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.46
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
15.38
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
57.69
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
42.31
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.86
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.24
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
24.29
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.08
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
2.46
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.8
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
98.92
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.66
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
6.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
2.59
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.08
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
175.51
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.08
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.24
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
40.27
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
5.8
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.47
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Classification on BNG(autos,10000,10)
0 runs - estimation_procedure: 33% Holdout set - target_feature: symboling
Supervised Data Stream Classification on BNG(autos,10000,10)
27 runs - estimation_procedure: Interleaved Test then Train - target_feature: symboling
Clustering on BNG(autos,10000,10)
0 runs
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(autos,10000,10)
0 runs - estimation_procedure: 50 times Clustering
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