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  • binarized_regression_problem Data Processing Machine Learning mythbusting_1 Research Statistics study_1 study_144 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').

22 features

binaryClass (target)nominal2 unique values
0 missing
DS_Namenominal22 unique values
0 missing
Tnumeric20 unique values
0 missing
Nnumeric20 unique values
0 missing
pnumeric18 unique values
0 missing
knumeric9 unique values
0 missing
Binnumeric7 unique values
0 missing
Costnumeric2 unique values
0 missing
SDrationumeric22 unique values
0 missing
correlnumeric21 unique values
24 missing
cancor1numeric22 unique values
0 missing
cancor2numeric12 unique values
240 missing
fract1numeric13 unique values
0 missing
fract2numeric10 unique values
240 missing
skewnessnumeric22 unique values
0 missing
kurtosisnumeric22 unique values
0 missing
Hcnumeric21 unique values
0 missing
Hxnumeric22 unique values
0 missing
MCxnumeric22 unique values
0 missing
EnAtrnumeric22 unique values
0 missing
NSRationumeric22 unique values
0 missing
Alg_Namenominal24 unique values
0 missing

107 properties

528
Number of instances (rows) of the dataset.
22
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
504
Number of missing values in the dataset.
264
Number of instances with at least one value missing.
19
Number of numeric attributes.
3
Number of nominal attributes.
3.83
Maximum skewness among attributes of the numeric type.
0.15
Minimum standard deviation of attributes of the numeric type.
4.34
Percentage of missing values.
8.03
Third quartile of kurtosis among attributes of the numeric type.
0.83
Average class difference between consecutive instances.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
14247.3
Maximum standard deviation of attributes of the numeric type.
10.23
Percentage of instances belonging to the least frequent class.
86.36
Percentage of numeric attributes.
22.67
Third quartile of means among attributes of the numeric type.
0.5
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.52
Average entropy of the attributes.
54
Number of instances belonging to the least frequent class.
13.64
Percentage of nominal attributes.
0.12
Third quartile of mutual information between the nominal attributes and the target attribute.
0.1
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
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.76
Mean kurtosis among attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.46
First quartile of entropy among attributes.
2.74
Third quartile of skewness among attributes of the numeric type.
0
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
812.1
Mean of means among attributes of the numeric type.
0.26
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.37
First quartile of kurtosis among attributes of the numeric type.
36.04
Third quartile of standard deviation of attributes of the numeric type.
0.5
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.12
Average mutual information between the nominal attributes and the target attribute.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.7
First quartile of means among attributes of the numeric type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.1
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
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
37.99
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.
0.11
First quartile of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
12.17
Standard deviation of the number of distinct values among attributes of the nominal type.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
16
Average number of distinct values among the attributes of the nominal type.
0.04
First quartile of skewness among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.54
Mean skewness among attributes of the numeric type.
0.33
First quartile of standard deviation of attributes of the numeric type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.1
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
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
89.77
Percentage of instances belonging to the most frequent class.
1053.15
Mean standard deviation of attributes of the numeric type.
4.52
Second quartile (Median) of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
474
Number of instances belonging to the most frequent class.
4.46
Minimal entropy among attributes.
1.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.48
Entropy of the target attribute values.
4.58
Maximum entropy among attributes.
-1.58
Minimum kurtosis among attributes of the numeric type.
1.87
Second quartile (Median) of means among attributes of the numeric type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
13.99
Maximum kurtosis among attributes of the numeric type.
0.14
Minimum of means among attributes of the numeric type.
0.12
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
10734.18
Maximum of means among attributes of the numeric type.
0.11
Minimal mutual information between the nominal attributes and the target attribute.
1.73
Second quartile (Median) of skewness among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.12
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
4.55
Percentage of binary attributes.
1.75
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
24
The maximum number of distinct values among attributes of the nominal type.
-1.51
Minimum skewness among attributes of the numeric type.
50
Percentage of instances having missing values.
4.58
Third quartile of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
4.11
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

15 tasks

472 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
219 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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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