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bodyfat

bodyfat

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 study_7 study_88 trivial
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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').

15 features

binaryClass (target)nominal2 unique values
0 missing
Densitynumeric218 unique values
0 missing
Agenumeric51 unique values
0 missing
Weightnumeric197 unique values
0 missing
Heightnumeric48 unique values
0 missing
Necknumeric90 unique values
0 missing
Chestnumeric174 unique values
0 missing
Abdomennumeric185 unique values
0 missing
Hipnumeric152 unique values
0 missing
Thighnumeric139 unique values
0 missing
Kneenumeric90 unique values
0 missing
Anklenumeric61 unique values
0 missing
Bicepsnumeric104 unique values
0 missing
Forearmnumeric77 unique values
0 missing
Wristnumeric44 unique values
0 missing

107 properties

252
Number of instances (rows) of the dataset.
15
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.
14
Number of numeric attributes.
1
Number of nominal attributes.
Maximum entropy among attributes.
-0.42
Minimum kurtosis among attributes of the numeric type.
41.74
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
59.54
Maximum kurtosis among attributes of the numeric type.
1.06
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
178.92
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.53
Second quartile (Median) of skewness among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
6.67
Percentage of binary attributes.
3.34
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
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-5.38
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.26
Maximum skewness among attributes of the numeric type.
0.02
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
5.82
Third quartile of kurtosis among attributes of the numeric type.
0.59
Average class difference between consecutive instances.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
29.39
Maximum standard deviation of attributes of the numeric type.
49.21
Percentage of instances belonging to the least frequent class.
93.33
Percentage of numeric attributes.
94.39
Third quartile of means among attributes of the numeric type.
1
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
124
Number of instances belonging to the least frequent class.
6.67
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0
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
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6.78
Mean kurtosis among attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.93
Third quartile of skewness among attributes of the numeric type.
0.99
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
59.04
Mean of means among attributes of the numeric type.
0.21
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.47
First quartile of kurtosis among attributes of the numeric type.
9.02
Third quartile of standard deviation of attributes of the numeric type.
1
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
27.27
First quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.99
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.21
First quartile of skewness among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
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
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.26
Mean skewness among attributes of the numeric type.
1.94
First quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.79
Percentage of instances belonging to the most frequent class.
6.42
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
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
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
128
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.66
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.

7 tasks

559 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
210 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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