Data
BNG(lymph,nominal,1000000)

BNG(lymph,nominal,1000000)

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

class (target)nominal4 unique values
0 missing
lymphaticsnominal4 unique values
0 missing
block_of_afferenominal2 unique values
0 missing
bl_of_lymph_cnominal2 unique values
0 missing
bl_of_lymph_snominal2 unique values
0 missing
by_passnominal2 unique values
0 missing
extravasatesnominal2 unique values
0 missing
regeneration_ofnominal2 unique values
0 missing
early_uptake_innominal2 unique values
0 missing
lym_nodes_diminnominal3 unique values
0 missing
lym_nodes_enlarnominal3 unique values
0 missing
changes_in_lymnominal3 unique values
0 missing
defect_in_nodenominal4 unique values
0 missing
changes_in_nodenominal4 unique values
0 missing
changes_in_strunominal8 unique values
0 missing
special_formsnominal3 unique values
0 missing
dislocation_ofnominal2 unique values
0 missing
exclusion_of_nonominal2 unique values
0 missing
no_of_nodes_innominal3 unique values
0 missing

108 properties

1000000
Number of instances (rows) of the dataset.
19
Number of attributes (columns) of the dataset.
4
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.
19
Number of nominal attributes.
0
Maximum kurtosis among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
0.08
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0
Second quartile (Median) of skewness among attributes of the numeric type.
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.54
The predictive accuracy obtained by always predicting the majority class.
0.26
Maximum mutual information between the nominal attributes and the target attribute.
0
Minimum skewness among attributes of the numeric type.
47.37
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
8
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of instances having missing values.
1.55
Third quartile of entropy among attributes.
0.46
Average class difference between consecutive instances.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
14.35
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0
Maximum skewness among attributes of the numeric type.
0.02
Percentage of instances belonging to the least frequent class.
0
Percentage of missing values.
0
Third quartile of kurtosis among attributes of the numeric type.
0.96
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
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Maximum standard deviation of attributes of the numeric type.
16553
Number 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.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.RandomTree -depth 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.13
Average entropy of the attributes.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
100
Percentage of nominal attributes.
0.12
Third quartile of mutual information between the nominal attributes and the target attribute.
0.8
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
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Mean kurtosis among attributes of the numeric type.
0.12
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.76
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
0.96
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
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Mean of means among attributes of the numeric type.
0.77
Kappa coefficient 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.
0.11
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.09
Average mutual information between the nominal attributes and the target attribute.
11.9
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
First quartile of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.8
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
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.94
Average number of distinct values among the attributes of the nominal type.
9
Number of binary attributes.
0.04
First quartile of mutual information between the nominal attributes and the target attribute.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.96
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
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean skewness among attributes of the numeric type.
0
First quartile of skewness among attributes of the numeric type.
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
1.47
Standard deviation of the number of distinct values among attributes of the nominal type.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean standard deviation of attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.8
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
54.35
Percentage of instances belonging to the most frequent class.
0.35
Minimal entropy among attributes.
1
Second quartile (Median) of entropy among attributes.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.25
Entropy of the target attribute values.
0.09
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
543512
Number of instances belonging to the most frequent class.
0
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
2.68
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
0.96
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

9 tasks

18 runs - estimation_procedure: 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 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - 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
45 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
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