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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_144 study_15 study_20 study_7
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

62 features

binaryClass (target)nominal2 unique values
0 missing
Instance_name (ignore)nominal3178 unique values
0 missing
attribute_1nominal5 unique values
0 missing
attribute_2nominal5 unique values
0 missing
attribute_3nominal4 unique values
0 missing
attribute_4nominal4 unique values
0 missing
attribute_5nominal4 unique values
0 missing
attribute_6nominal4 unique values
0 missing
attribute_7nominal4 unique values
0 missing
attribute_8nominal4 unique values
0 missing
attribute_9nominal4 unique values
0 missing
attribute_10nominal4 unique values
0 missing
attribute_11nominal4 unique values
0 missing
attribute_12nominal4 unique values
0 missing
attribute_13nominal4 unique values
0 missing
attribute_14nominal5 unique values
0 missing
attribute_15nominal4 unique values
0 missing
attribute_16nominal4 unique values
0 missing
attribute_17nominal4 unique values
0 missing
attribute_18nominal4 unique values
0 missing
attribute_19nominal5 unique values
0 missing
attribute_20nominal5 unique values
0 missing
attribute_21nominal5 unique values
0 missing
attribute_22nominal5 unique values
0 missing
attribute_23nominal5 unique values
0 missing
attribute_24nominal5 unique values
0 missing
attribute_25nominal5 unique values
0 missing
attribute_26nominal5 unique values
0 missing
attribute_27nominal5 unique values
0 missing
attribute_28nominal5 unique values
0 missing
attribute_29nominal5 unique values
0 missing
attribute_30nominal5 unique values
0 missing
attribute_31nominal5 unique values
0 missing
attribute_32nominal5 unique values
0 missing
attribute_33nominal5 unique values
0 missing
attribute_34nominal5 unique values
0 missing
attribute_35nominal6 unique values
0 missing
attribute_36nominal6 unique values
0 missing
attribute_37nominal5 unique values
0 missing
attribute_38nominal5 unique values
0 missing
attribute_39nominal5 unique values
0 missing
attribute_40nominal5 unique values
0 missing
attribute_41nominal5 unique values
0 missing
attribute_42nominal5 unique values
0 missing
attribute_43nominal5 unique values
0 missing
attribute_44nominal5 unique values
0 missing
attribute_45nominal5 unique values
0 missing
attribute_46nominal5 unique values
0 missing
attribute_47nominal5 unique values
0 missing
attribute_48nominal5 unique values
0 missing
attribute_49nominal5 unique values
0 missing
attribute_50nominal5 unique values
0 missing
attribute_51nominal5 unique values
0 missing
attribute_52nominal5 unique values
0 missing
attribute_53nominal5 unique values
0 missing
attribute_54nominal5 unique values
0 missing
attribute_55nominal5 unique values
0 missing
attribute_56nominal5 unique values
0 missing
attribute_57nominal5 unique values
0 missing
attribute_58nominal5 unique values
0 missing
attribute_59nominal5 unique values
0 missing
attribute_60nominal5 unique values
0 missing

107 properties

3190
Number of instances (rows) of the dataset.
62
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.
62
Number of nominal attributes.
1
Average class difference between consecutive instances.
0.5
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
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.
0.96
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
48.12
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.05
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.98
Average entropy of the attributes.
1535
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
0.91
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
0.5
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
Mean kurtosis among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.99
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Third quartile of standard deviation of 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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.08
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
0.96
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.03
Average mutual information between the nominal attributes and the target attribute.
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.91
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
0.5
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
75.93
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.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.86
Kappa coefficient 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
0.6
Standard deviation of the number of distinct values among attributes of the nominal type.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.74
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.96
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.91
Kappa coefficient 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.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.91
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
0.2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
51.88
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
2
Second quartile (Median) of entropy among attributes.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1655
Number of instances belonging to the most frequent class.
1.67
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.01
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.18
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
0.35
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
1.61
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
38.85
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
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.
2
Third quartile of entropy among attributes.

7 tasks

135 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 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
0 runs - estimation_procedure: 50 times Clustering
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