Data
pbc

pbc

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized mythbusting_1 study_1 study_123 study_15 study_20 study_41
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

19 features

binaryClass (target)nominal2 unique values
0 missing
Dnominal2 unique values
0 missing
Z1nominal2 unique values
106 missing
Z2numeric345 unique values
0 missing
Z3nominal2 unique values
106 missing
Z4nominal2 unique values
106 missing
Z5nominal2 unique values
106 missing
Z6nominal2 unique values
106 missing
Z7nominal3 unique values
0 missing
Z8numeric98 unique values
0 missing
Z9numeric201 unique values
134 missing
Z10numeric154 unique values
0 missing
Z11numeric158 unique values
108 missing
Z12numeric295 unique values
106 missing
Z13numeric179 unique values
106 missing
Z14numeric146 unique values
136 missing
Z15numeric243 unique values
11 missing
Z16numeric48 unique values
2 missing
Z17nominal4 unique values
106 missing

107 properties

418
Number of instances (rows) of the dataset.
19
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
1239
Number of missing values in the dataset.
142
Number of instances with at least one value missing.
10
Number of numeric attributes.
9
Number of nominal attributes.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2140.39
Maximum standard deviation of attributes of the numeric type.
44.98
Percentage of instances belonging to the least frequent class.
52.63
Percentage of numeric attributes.
285.15
Third quartile of means among attributes of the numeric type.
0.69
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
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
Average entropy of the attributes.
188
Number of instances belonging to the least frequent class.
47.37
Percentage of nominal attributes.
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
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
0.38
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6.67
Mean kurtosis among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.79
First quartile of entropy among attributes.
2.79
Third quartile of skewness among attributes of the numeric type.
0.33
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
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
302.23
Mean of means among attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.79
First quartile of kurtosis among attributes of the numeric type.
131.73
Third quartile of standard deviation of attributes of the numeric type.
0.69
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
0.38
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.04
Average mutual information between the nominal attributes and the target attribute.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
8.92
First quartile of means among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
25.53
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
7
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.33
Average number of distinct values among the attributes of the nominal type.
0.49
First quartile of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.69
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
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.79
Mean skewness among attributes of the numeric type.
3.56
First quartile of standard deviation of attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.4
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.02
Percentage of instances belonging to the most frequent class.
269.44
Mean standard deviation of attributes of the numeric type.
0.95
Second quartile (Median) of entropy among attributes.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
230
Number of instances belonging to the most frequent class.
0.42
Minimal entropy among attributes.
7.84
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
1.61
Maximum entropy among attributes.
-0.62
Minimum kurtosis among attributes of the numeric type.
110.1
Second quartile (Median) of means among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
14.34
Maximum kurtosis among attributes of the numeric type.
3.22
Minimum of means among attributes of the numeric type.
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.39
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1982.66
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2.26
Second quartile (Median) of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.06
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
36.84
Percentage of binary attributes.
60.92
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.05
Number of attributes divided by the number of instances.
27.94
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
-0.47
Minimum skewness among attributes of the numeric type.
33.97
Percentage of instances having missing values.
0.99
Third quartile of entropy among attributes.
0.38
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.41
Maximum skewness among attributes of the numeric type.
0.42
Minimum standard deviation of attributes of the numeric type.
15.6
Percentage of missing values.
10.48
Third quartile of kurtosis among attributes of the numeric type.
0.63
Average class difference between consecutive instances.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

7 tasks

508 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
215 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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