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BNG(anneal.ORIG,10000,5)

BNG(anneal.ORIG,10000,5)

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39 features

class (target)nominal6 unique values
0 missing
phosnominal1 unique values
0 missing
chromnominal1 unique values
0 missing
cbondnominal1 unique values
0 missing
marvinominal1 unique values
0 missing
exptlnominal1 unique values
0 missing
ferronominal1 unique values
0 missing
corrnominal1 unique values
0 missing
blue%2Fbright%2Fvarn%2Fcleannominal4 unique values
0 missing
lustrenominal1 unique values
0 missing
jurofmnominal1 unique values
0 missing
snominal1 unique values
0 missing
pnominal1 unique values
0 missing
shapenominal2 unique values
0 missing
thicknumeric732215 unique values
0 missing
widthnumeric593569 unique values
0 missing
lennumeric215893 unique values
0 missing
oilnominal2 unique values
0 missing
borenominal4 unique values
0 missing
packingnominal3 unique values
0 missing
surface-finishnominal2 unique values
0 missing
product-typenominal3 unique values
0 missing
steelnominal8 unique values
0 missing
carbonnumeric186024 unique values
0 missing
hardnessnumeric167245 unique values
0 missing
temper_rollingnominal1 unique values
0 missing
conditionnominal3 unique values
0 missing
formabilitynominal5 unique values
0 missing
strengthnumeric246047 unique values
0 missing
non-ageingnominal1 unique values
0 missing
familynominal9 unique values
0 missing
surface-qualitynominal4 unique values
0 missing
enamelabilitynominal5 unique values
0 missing
bcnominal1 unique values
0 missing
bfnominal1 unique values
0 missing
btnominal1 unique values
0 missing
bw%2Fmenominal2 unique values
0 missing
blnominal1 unique values
0 missing
mnominal1 unique values
0 missing
Show all 39 features

107 properties

NumberOfInstances
1000000
Number of instances (rows) of the dataset.
NumberOfFeatures
39
Number of attributes (columns) of the dataset.
NumberOfClasses
6
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
6
Number of numeric attributes.
NumberOfSymbolicFeatures
33
Number of nominal attributes.
AutoCorrelation
0.6
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.86
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
0.15
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
0.57
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
0.86
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
0.15
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
0.57
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
0.86
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
0.15
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
0.57
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
1.2
Entropy of the target attribute values.
DecisionStumpAUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
53.33
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
75.97
Percentage of instances belonging to the most frequent class.
MajorityClassSize
759652
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
2.28
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.65
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1040.42
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.16
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
9
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.85
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1733.23
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.47
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.23
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
334.65
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.02
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
19.93
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.42
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.13
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
402.4
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
-0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.87
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
1.23
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.07
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.89
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.06
Percentage of instances belonging to the least frequent class.
MinorityClassSize
555
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
0.22
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
10.26
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
15.38
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
84.62
Percentage of nominal attributes.
Quartile1AttributeEntropy
0
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.81
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
7.98
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.69
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
16.59
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.04
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
85.21
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.21
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
125.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.88
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.4
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
849.29
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.63
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
739.36
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
2.14
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

22 tasks

Supervised Classification on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on BNG(anneal.ORIG,10000,5)
28 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on BNG(anneal.ORIG,10000,5)
0 runs
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
Clustering on BNG(anneal.ORIG,10000,5)
0 runs - estimation_procedure: 50 times Clustering
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