Data
fri_c1_500_50

fri_c1_500_50

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 study_88
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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').

51 features

binaryClass (target)nominal2 unique values
0 missing
oz1numeric500 unique values
0 missing
oz2numeric500 unique values
0 missing
oz3numeric500 unique values
0 missing
oz4numeric500 unique values
0 missing
oz5numeric500 unique values
0 missing
oz6numeric500 unique values
0 missing
oz7numeric500 unique values
0 missing
oz8numeric499 unique values
0 missing
oz9numeric500 unique values
0 missing
oz10numeric500 unique values
0 missing
oz11numeric500 unique values
0 missing
oz12numeric500 unique values
0 missing
oz13numeric500 unique values
0 missing
oz14numeric499 unique values
0 missing
oz15numeric500 unique values
0 missing
oz16numeric500 unique values
0 missing
oz17numeric500 unique values
0 missing
oz18numeric500 unique values
0 missing
oz19numeric500 unique values
0 missing
oz20numeric500 unique values
0 missing
oz21numeric500 unique values
0 missing
oz22numeric500 unique values
0 missing
oz23numeric500 unique values
0 missing
oz24numeric500 unique values
0 missing
oz25numeric500 unique values
0 missing
oz26numeric500 unique values
0 missing
oz27numeric500 unique values
0 missing
oz28numeric500 unique values
0 missing
oz29numeric500 unique values
0 missing
oz30numeric500 unique values
0 missing
oz31numeric500 unique values
0 missing
oz32numeric500 unique values
0 missing
oz33numeric500 unique values
0 missing
oz34numeric500 unique values
0 missing
oz35numeric500 unique values
0 missing
oz36numeric500 unique values
0 missing
oz37numeric500 unique values
0 missing
oz38numeric500 unique values
0 missing
oz39numeric500 unique values
0 missing
oz40numeric500 unique values
0 missing
oz41numeric500 unique values
0 missing
oz42numeric500 unique values
0 missing
oz43numeric500 unique values
0 missing
oz44numeric500 unique values
0 missing
oz45numeric500 unique values
0 missing
oz46numeric500 unique values
0 missing
oz47numeric500 unique values
0 missing
oz48numeric500 unique values
0 missing
oz49numeric500 unique values
0 missing
oz50numeric500 unique values
0 missing

107 properties

500
Number of instances (rows) of the dataset.
51
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.
50
Number of numeric attributes.
1
Number of nominal attributes.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.05
Third quartile of skewness among attributes of the numeric type.
0.56
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
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1.18
Mean kurtosis among attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.22
First quartile of kurtosis among attributes of the numeric type.
1
Third quartile of standard deviation of attributes of the numeric type.
0.82
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
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0
Mean of means among attributes of the numeric type.
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
First quartile of means among attributes of the numeric type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.22
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
0.38
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.56
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
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.82
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.
-0.07
First quartile of skewness among attributes of the numeric type.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.82
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.
0.19
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.
1
First quartile of standard deviation of attributes of the numeric type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.22
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.01
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.56
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
0.43
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
52.4
Percentage of instances belonging to the most frequent class.
1
Mean standard deviation of attributes of the numeric type.
-1.17
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
262
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0
Second quartile (Median) of means among attributes of the numeric type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.34
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.95
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
-0.01
Second quartile (Median) of skewness among attributes of the numeric type.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.96
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.1
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.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.38
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.
-0.14
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
-1.15
Third quartile of kurtosis among attributes of the numeric type.
0.52
Average class difference between consecutive instances.
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.14
Maximum skewness among attributes of the numeric type.
1
Minimum standard deviation of attributes of the numeric type.
98.04
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
0.82
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
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Maximum standard deviation of attributes of the numeric type.
47.6
Percentage of instances belonging to the least frequent class.
1.96
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.22
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
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
238
Number of instances belonging to the least frequent class.

7 tasks

561 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
220 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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