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mfeat-zernike

mfeat-zernike

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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

48 features

binaryClass (target)nominal2 unique values
0 missing
att26numeric1984 unique values
0 missing
att25numeric1918 unique values
0 missing
att27numeric1991 unique values
0 missing
att28numeric1992 unique values
0 missing
att29numeric1994 unique values
0 missing
att30numeric1938 unique values
0 missing
att31numeric1988 unique values
0 missing
att32numeric1994 unique values
0 missing
att33numeric1994 unique values
0 missing
att34numeric1963 unique values
0 missing
att35numeric1989 unique values
0 missing
att36numeric1993 unique values
0 missing
att37numeric1992 unique values
0 missing
att38numeric1978 unique values
0 missing
att39numeric1993 unique values
0 missing
att40numeric1994 unique values
0 missing
att41numeric1962 unique values
0 missing
att42numeric1994 unique values
0 missing
att43numeric1993 unique values
0 missing
att44numeric1993 unique values
0 missing
att45numeric1992 unique values
0 missing
att46numeric1992 unique values
0 missing
att47numeric1989 unique values
0 missing
att13numeric1994 unique values
0 missing
att2numeric1958 unique values
0 missing
att3numeric1990 unique values
0 missing
att4numeric1994 unique values
0 missing
att5numeric1994 unique values
0 missing
att6numeric1994 unique values
0 missing
att7numeric1994 unique values
0 missing
att8numeric1893 unique values
0 missing
att9numeric1975 unique values
0 missing
att10numeric1990 unique values
0 missing
att11numeric1993 unique values
0 missing
att12numeric1994 unique values
0 missing
att1numeric1885 unique values
0 missing
att14numeric1897 unique values
0 missing
att15numeric1978 unique values
0 missing
att16numeric1991 unique values
0 missing
att17numeric1992 unique values
0 missing
att18numeric1994 unique values
0 missing
att19numeric1992 unique values
0 missing
att20numeric1907 unique values
0 missing
att21numeric1981 unique values
0 missing
att22numeric1989 unique values
0 missing
att23numeric1994 unique values
0 missing
att24numeric1994 unique values
0 missing
Show all 48 features

107 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
48
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
47
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
1
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
0.02
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
0.92
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
0.02
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
0.92
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
0.02
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
0.92
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
0.47
Entropy of the target attribute values.
DecisionStumpAUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
90
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1800
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
4.37
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
508.9
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
1.79
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
124.19
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.74
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
88.64
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
0.79
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
40.13
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.99
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.08
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
0.01
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.07
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
10
Percentage of instances belonging to the least frequent class.
MinorityClassSize
200
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
2.08
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
97.92
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
2.08
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.46
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
7.54
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
0.38
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
3.76
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
69.88
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
0.74
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
37.88
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.87
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
126.83
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
1.25
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
65.21
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.84
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

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