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  • binarized_regression_problem mythbusting_1 study_1 study_144 study_15 study_20 study_41 study_293
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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
fract1numeric13 unique values
0 missing
Alg_Namenominal24 unique values
0 missing
NSRationumeric22 unique values
0 missing
EnAtrnumeric22 unique values
0 missing
MCxnumeric22 unique values
0 missing
Hxnumeric22 unique values
0 missing
Hcnumeric21 unique values
0 missing
kurtosisnumeric22 unique values
0 missing
skewnessnumeric22 unique values
0 missing
fract2numeric10 unique values
240 missing
DS_Namenominal22 unique values
0 missing
cancor2numeric12 unique values
240 missing
cancor1numeric22 unique values
0 missing
correlnumeric21 unique values
24 missing
SDrationumeric22 unique values
0 missing
Costnumeric2 unique values
0 missing
Binnumeric7 unique values
0 missing
knumeric9 unique values
0 missing
pnumeric18 unique values
0 missing
Nnumeric20 unique values
0 missing
Tnumeric20 unique values
0 missing
Show all 22 features

107 properties

NumberOfInstances
528
Number of instances (rows) of the dataset.
NumberOfFeatures
22
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
504
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
264
Number of instances with at least one value missing.
NumberOfNumericFeatures
19
Number of numeric attributes.
NumberOfSymbolicFeatures
3
Number of nominal attributes.
AutoCorrelation
0.83
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
0.48
Entropy of the target attribute values.
DecisionStumpAUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.04
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
4.11
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
89.77
Percentage of instances belonging to the most frequent class.
MajorityClassSize
474
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.58
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
13.99
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
10734.18
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.12
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
24
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
3.83
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
14247.3
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
4.52
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
3.76
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
812.1
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.12
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
37.99
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
16
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.54
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1053.15
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
4.46
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.58
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.14
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.11
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.51
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.15
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
10.23
Percentage of instances belonging to the least frequent class.
MinorityClassSize
54
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
4.55
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
50
Percentage of instances having missing values.
PercentageOfMissingValues
4.34
Percentage of missing values.
PercentageOfNumericFeatures
86.36
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
13.64
Percentage of nominal attributes.
Quartile1AttributeEntropy
4.46
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.37
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.7
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.11
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.04
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.33
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
4.52
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
1.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1.87
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.12
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.73
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1.75
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
4.58
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
8.03
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
22.67
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.12
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
2.74
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
36.04
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
12.17
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on meta
472 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on meta
219 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on meta
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
Supervised Data Stream Classification on meta
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
Clustering on meta
0 runs
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
Clustering on meta
0 runs - estimation_procedure: 50 times Clustering
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