Data
BNG(colic,nominal,1000000)

BNG(colic,nominal,1000000)

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

surgical_lesion (target)nominal2 unique values
0 missing
surgerynominal2 unique values
0 missing
Agenominal2 unique values
0 missing
rectal_temperaturenominal3 unique values
0 missing
pulsenominal3 unique values
0 missing
respiratory_ratenominal3 unique values
0 missing
temp_extremitiesnominal4 unique values
0 missing
peripheral_pulsenominal4 unique values
0 missing
mucous_membranesnominal6 unique values
0 missing
capillary_refill_timenominal3 unique values
0 missing
painnominal5 unique values
0 missing
peristalsisnominal4 unique values
0 missing
abdominal_distensionnominal4 unique values
0 missing
nasogastric_tubenominal3 unique values
0 missing
nasogastric_refluxnominal3 unique values
0 missing
nasogastric_reflux_PHnominal3 unique values
0 missing
rectal_examinationnominal4 unique values
0 missing
abdomennominal5 unique values
0 missing
packed_cell_volumenominal3 unique values
0 missing
total_proteinnominal3 unique values
0 missing
abdominocentesis_appearancenominal3 unique values
0 missing
abdomcentesis_total_proteinnominal3 unique values
0 missing
outcomenominal3 unique values
0 missing

107 properties

1000000
Number of instances (rows) of the dataset.
23
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.
23
Number of nominal attributes.
6
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.
1.66
Third quartile of entropy among attributes.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
20.56
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
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.
0.53
Average class difference between consecutive instances.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
37.03
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.93
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.24
Average entropy of the attributes.
370347
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
0.12
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Mean kurtosis among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.95
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.74
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
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.14
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.
0.93
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.05
Average mutual information between the nominal attributes and the target attribute.
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.12
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
25.86
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.74
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
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.39
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.93
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
0.99
Standard deviation of the number of distinct values among attributes of the nominal type.
0.1
Error rate 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.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.12
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
1.14
Second quartile (Median) of entropy among attributes.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.74
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
0.11
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
62.97
Percentage of instances belonging to the most frequent class.
0.4
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
Entropy of the target attribute values.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
629653
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.31
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
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.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.28
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
13.04
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.87
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.

9 tasks

25 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: surgical_lesion
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: surgical_lesion
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: surgical_lesion
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: surgical_lesion
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: surgical_lesion
47 runs - estimation_procedure: Interleaved Test then Train - target_feature: surgical_lesion
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task