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pol

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized_regression_problem mythbusting_1 study_1 study_15 study_20 study_41 study_7
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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').

49 features

binaryClass (target)nominal2 unique values
0 missing
f1numeric1 unique values
0 missing
f2numeric1 unique values
0 missing
f3numeric1 unique values
0 missing
f4numeric1 unique values
0 missing
f5numeric184 unique values
0 missing
f6numeric118 unique values
0 missing
f7numeric114 unique values
0 missing
f8numeric106 unique values
0 missing
f9numeric80 unique values
0 missing
f10numeric1 unique values
0 missing
f11numeric1 unique values
0 missing
f12numeric1 unique values
0 missing
f13numeric97 unique values
0 missing
f14numeric117 unique values
0 missing
f15numeric121 unique values
0 missing
f16numeric120 unique values
0 missing
f17numeric120 unique values
0 missing
f18numeric123 unique values
0 missing
f19numeric102 unique values
0 missing
f20numeric86 unique values
0 missing
f21numeric85 unique values
0 missing
f22numeric88 unique values
0 missing
f23numeric79 unique values
0 missing
f24numeric63 unique values
0 missing
f25numeric68 unique values
0 missing
f26numeric68 unique values
0 missing
f27numeric65 unique values
0 missing
f28numeric64 unique values
0 missing
f29numeric62 unique values
0 missing
f30numeric44 unique values
0 missing
f31numeric43 unique values
0 missing
f32numeric42 unique values
0 missing
f33numeric38 unique values
0 missing
f34numeric1 unique values
0 missing
f35numeric1 unique values
0 missing
f36numeric1 unique values
0 missing
f37numeric1 unique values
0 missing
f38numeric1 unique values
0 missing
f39numeric1 unique values
0 missing
f40numeric1 unique values
0 missing
f41numeric1 unique values
0 missing
f42numeric1 unique values
0 missing
f43numeric1 unique values
0 missing
f44numeric1 unique values
0 missing
f45numeric1 unique values
0 missing
f46numeric1 unique values
0 missing
f47numeric1 unique values
0 missing
f48numeric1 unique values
0 missing

107 properties

15000
Number of instances (rows) of the dataset.
49
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.
48
Number of numeric attributes.
1
Number of nominal attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.89
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
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.98
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.
1.86
First quartile of skewness among attributes of the numeric type.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.03
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.
4.78
Mean skewness among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.05
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
6.52
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.89
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
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
66.39
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
16.99
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Entropy of the target attribute values.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
9959
Number of instances belonging to the most frequent class.
-0.07
Minimum kurtosis among attributes of the numeric type.
0.93
Second quartile (Median) of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
147.26
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
3.92
Second quartile (Median) of skewness among attributes of the numeric type.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
110
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
2.04
Percentage of binary attributes.
3.13
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.94
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.
Maximum mutual information between the nominal attributes and the target attribute.
0.31
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.05
Error rate 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.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
69.84
Third quartile of kurtosis among attributes of the numeric type.
0.56
Average class difference between consecutive instances.
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
11.62
Maximum skewness among attributes of the numeric type.
33.61
Percentage of instances belonging to the least frequent class.
97.96
Percentage of numeric attributes.
12.01
Third quartile of means among attributes of the numeric type.
0.98
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
35.25
Maximum standard deviation of attributes of the numeric type.
5041
Number of instances belonging to the least frequent class.
2.04
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.05
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
7.8
Third quartile of skewness among attributes of the numeric type.
0.89
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
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
34.89
Mean kurtosis among attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.35
First quartile of kurtosis among attributes of the numeric type.
11.51
Third quartile of standard deviation of attributes of the numeric type.
0.98
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
19.43
Mean of means among attributes of the numeric type.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.05
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.

6 tasks

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