Data
BNG(anneal,nominal,1000000)

BNG(anneal,nominal,1000000)

active ARFF Publicly available Visibility: public Uploaded 08-04-2014 by Jan van Rijn
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39 features

class (target)nominal6 unique values
0 missing
familynominal10 unique values
0 missing
product-typenominal3 unique values
0 missing
steelnominal9 unique values
0 missing
carbonnominal3 unique values
0 missing
hardnessnominal3 unique values
0 missing
temper_rollingnominal2 unique values
0 missing
conditionnominal4 unique values
0 missing
formabilitynominal6 unique values
0 missing
strengthnominal3 unique values
0 missing
non-ageingnominal2 unique values
0 missing
surface-finishnominal3 unique values
0 missing
surface-qualitynominal5 unique values
0 missing
enamelabilitynominal6 unique values
0 missing
bcnominal2 unique values
0 missing
bfnominal2 unique values
0 missing
btnominal2 unique values
0 missing
bw%2Fmenominal3 unique values
0 missing
blnominal2 unique values
0 missing
mnominal2 unique values
0 missing
chromnominal2 unique values
0 missing
phosnominal2 unique values
0 missing
cbondnominal2 unique values
0 missing
marvinominal2 unique values
0 missing
exptlnominal2 unique values
0 missing
ferronominal2 unique values
0 missing
corrnominal2 unique values
0 missing
blue%2Fbright%2Fvarn%2Fcleannominal5 unique values
0 missing
lustrenominal2 unique values
0 missing
jurofmnominal2 unique values
0 missing
snominal2 unique values
0 missing
pnominal2 unique values
0 missing
shapenominal2 unique values
0 missing
thicknominal3 unique values
0 missing
widthnominal3 unique values
0 missing
lennominal3 unique values
0 missing
oilnominal3 unique values
0 missing
borenominal4 unique values
0 missing
packingnominal4 unique values
0 missing

119 properties

1000000
Number of instances (rows) of the dataset.
39
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.
0
Number of numeric attributes.
39
Number of nominal attributes.
0.56
Second quartile (Median) of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
19.99
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.89
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Maximum of means among attributes of the numeric type.
0
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.95
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
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.39
Maximum mutual information between the nominal attributes and the target attribute.
0
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
0.11
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
10
The maximum number of distinct values among attributes of the nominal type.
0
Minimum of means among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.72
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
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
0
Maximum skewness among attributes of the numeric type.
0
Maximum standard deviation of attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Second quartile (Median) of skewness among attributes of the numeric type.
0.95
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
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.63
Average entropy of the attributes.
2
The minimal number of distinct values among attributes of the nominal type.
48.72
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.11
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
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.91
Third quartile of entropy among attributes.
0.72
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
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0
Third quartile of kurtosis among attributes of the numeric type.
0.95
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
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean kurtosis among attributes of the numeric type.
0
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
0.11
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean of means among attributes of the numeric type.
597
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.08
Third quartile of mutual information between the nominal attributes and the target attribute.
0.72
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.06
Average mutual information between the nominal attributes and the target attribute.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.09
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
1.2
Entropy of the target attribute values.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
75.95
Percentage of instances belonging to the most frequent class.
9.38
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.12
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of kurtosis among attributes of the numeric type.
0
Third quartile of standard deviation of attributes of the numeric type.
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
759513
Number of instances belonging to the most frequent class.
3.18
Average number of distinct values among the attributes of the nominal type.
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of means among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.17
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
2.18
Maximum entropy among attributes.
0
Mean skewness among attributes of the numeric type.
19
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.6
Average class difference between consecutive instances.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Mean standard deviation of attributes of the numeric type.
0
First quartile of skewness among attributes of the numeric type.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.76
The predictive accuracy obtained by always predicting the majority class.
0.03
Minimal entropy among attributes.
0
First quartile of standard deviation of attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
Number of attributes divided by the number of instances.
0
Maximum kurtosis among attributes of the numeric type.

12 tasks

21 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
122 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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