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fri_c0_1000_50

fri_c0_1000_50

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Felicia West
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  • binarized_regression_problem mythbusting_1 study_1 study_15 study_20 study_41 study_7 study_236 study_388 study_388 study_388
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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
oz27numeric1000 unique values
0 missing
oz26numeric1000 unique values
0 missing
oz28numeric1000 unique values
0 missing
oz29numeric1000 unique values
0 missing
oz30numeric1000 unique values
0 missing
oz31numeric1000 unique values
0 missing
oz32numeric1000 unique values
0 missing
oz33numeric1000 unique values
0 missing
oz34numeric1000 unique values
0 missing
oz35numeric1000 unique values
0 missing
oz36numeric1000 unique values
0 missing
oz37numeric1000 unique values
0 missing
oz38numeric1000 unique values
0 missing
oz39numeric1000 unique values
0 missing
oz40numeric1000 unique values
0 missing
oz41numeric1000 unique values
0 missing
oz42numeric1000 unique values
0 missing
oz43numeric998 unique values
0 missing
oz44numeric1000 unique values
0 missing
oz45numeric1000 unique values
0 missing
oz46numeric1000 unique values
0 missing
oz47numeric1000 unique values
0 missing
oz48numeric999 unique values
0 missing
oz49numeric1000 unique values
0 missing
oz50numeric1000 unique values
0 missing
oz14numeric1000 unique values
0 missing
oz2numeric1000 unique values
0 missing
oz3numeric1000 unique values
0 missing
oz4numeric1000 unique values
0 missing
oz5numeric999 unique values
0 missing
oz6numeric1000 unique values
0 missing
oz7numeric1000 unique values
0 missing
oz8numeric1000 unique values
0 missing
oz9numeric999 unique values
0 missing
oz10numeric1000 unique values
0 missing
oz11numeric1000 unique values
0 missing
oz12numeric1000 unique values
0 missing
oz13numeric1000 unique values
0 missing
oz1numeric1000 unique values
0 missing
oz15numeric1000 unique values
0 missing
oz16numeric1000 unique values
0 missing
oz17numeric1000 unique values
0 missing
oz18numeric1000 unique values
0 missing
oz19numeric1000 unique values
0 missing
oz20numeric1000 unique values
0 missing
oz21numeric1000 unique values
0 missing
oz22numeric999 unique values
0 missing
oz23numeric999 unique values
0 missing
oz24numeric1000 unique values
0 missing
oz25numeric1000 unique values
0 missing
Show all 51 features

107 properties

NumberOfInstances
1000
Number of instances (rows) of the dataset.
NumberOfFeatures
51
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
50
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.53
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.84
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
0.18
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
0.63
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
0.84
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
0.18
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
0.63
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
0.84
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
0.18
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
0.63
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
1
Entropy of the target attribute values.
DecisionStumpAUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.05
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
51
Percentage of instances belonging to the most frequent class.
MajorityClassSize
510
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
-1.11
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.08
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-1.2
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0.01
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.28
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0
Minimum of means among attributes of the numeric type.
MinMutualInformation
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
-0.11
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
49
Percentage of instances belonging to the least frequent class.
MinorityClassSize
490
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
1.96
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
98.04
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
1.96
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.22
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.05
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-1.16
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.02
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on fri_c0_1000_50
439 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on fri_c0_1000_50
207 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on fri_c0_1000_50
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Data Stream Classification on fri_c0_1000_50
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
Clustering on fri_c0_1000_50
0 runs
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
Clustering on fri_c0_1000_50
0 runs - estimation_procedure: 50 times Clustering
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