Data
BNG(trains)

BNG(trains)

active ARFF Publicly available Visibility: public Uploaded 09-04-2014 by Jan van Rijn
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  • artificial BNG study_16 study_69
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33 features

class (target)nominal2 unique values
0 missing
Number_of_carsnominal3 unique values
0 missing
Number_of_different_loadsnominal4 unique values
0 missing
num_wheels_2nominal2 unique values
0 missing
length_2nominal2 unique values
0 missing
shape_2nominal5 unique values
0 missing
num_loads_2nominal2 unique values
0 missing
load_shape_2nominal3 unique values
0 missing
num_wheels_3nominal2 unique values
0 missing
length_3nominal2 unique values
0 missing
shape_3nominal8 unique values
0 missing
num_loads_3nominal2 unique values
0 missing
load_shape_3nominal3 unique values
0 missing
num_wheels_4nominal2 unique values
0 missing
length_4nominal2 unique values
0 missing
shape_4nominal4 unique values
0 missing
num_loads_4nominal3 unique values
0 missing
load_shape_4nominal4 unique values
0 missing
num_wheels_5nominal1 unique values
0 missing
length_5nominal1 unique values
0 missing
shape_5nominal2 unique values
0 missing
num_loads_5nominal1 unique values
0 missing
load_shape_5nominal2 unique values
0 missing
Rectangle_next_to_rectanglenominal2 unique values
0 missing
Rectangle_next_to_trianglenominal2 unique values
0 missing
Rectangle_next_to_hexagonnominal1 unique values
0 missing
Rectangle_next_to_circlenominal2 unique values
0 missing
Triangle_next_to_trianglenominal2 unique values
0 missing
Triangle_next_to_hexagonnominal2 unique values
0 missing
Triangle_next_to_circlenominal2 unique values
0 missing
Hexagon_next_to_hexagonnominal1 unique values
0 missing
Hexagon_next_to_circlenominal2 unique values
0 missing
Circle_next_to_circlenominal1 unique values
0 missing

107 properties

1000000
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
2
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.
33
Number of nominal attributes.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
49.89
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.97
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
0.1
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 .00001
0.99
Average entropy of the attributes.
498881
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
0.08
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
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.65
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.85
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
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.97
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
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.03
Average mutual information between the nominal attributes and the target attribute.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.08
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
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
30.86
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
18
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.85
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
1.39
Standard deviation of the number of distinct values among attributes of the nominal type.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.39
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.08
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
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.11
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
0.96
Second quartile (Median) of entropy among attributes.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.85
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
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
501119
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.99
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
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.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Number of attributes divided by the number of instances.
0.2
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
54.55
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
32.04
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
8
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.51
Third quartile of entropy among attributes.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.

16 tasks

21 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
1 runs - estimation_procedure: 33% Holdout set - 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 times 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: 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
0 runs - estimation_procedure: 10-fold Learning Curve - 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
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
312 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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