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  • binarized mythbusting_1 study_1 study_123 study_15 study_20 study_41 study_7 study_88 study_236
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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').

8 features

binaryClass (target)nominal2 unique values
0 missing
vendornominal30 unique values
0 missing
MYCTnumeric60 unique values
0 missing
MMINnumeric25 unique values
0 missing
MMAXnumeric23 unique values
0 missing
CACHnumeric22 unique values
0 missing
CHMINnumeric15 unique values
0 missing
CHMAXnumeric31 unique values
0 missing
Show all 8 features

107 properties

NumberOfInstances
209
Number of instances (rows) of the dataset.
NumberOfFeatures
8
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
6
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
AutoCorrelation
0.85
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
0.06
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
0.84
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
0.96
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
CfsSubsetEval_NaiveBayesErrRate
0.06
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
0.84
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
0.96
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
CfsSubsetEval_kNN1NErrRate
0.06
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
0.84
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
0.82
Entropy of the target attribute values.
DecisionStumpAUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.04
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
2.77
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
74.64
Percentage of instances belonging to the most frequent class.
MajorityClassSize
156
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.42
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
22.47
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
11796.15
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.3
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
30
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
4.03
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
11726.56
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
4.42
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
13.2
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
2486.02
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.3
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
13.98
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
3.11
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
2656.5
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
4.42
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
5.9
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
4.7
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.3
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
2.14
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
6.82
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
25.36
Percentage of instances belonging to the least frequent class.
MinorityClassSize
53
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
12.5
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
75
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
25
Percentage of nominal attributes.
Quartile1AttributeEntropy
4.42
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
6.77
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
14.88
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.3
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
2.44
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
21.2
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
4.42
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
13.08
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
114.51
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.3
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
3.17
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
150.45
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
4.42
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
18.83
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
5100.02
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.3
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
3.7
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
5840.7
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
19.8
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

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