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webdata_wXa

webdata_wXa

active Sparse_ARFF Publicly available Visibility: public Uploaded 29-08-2014 by David
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Author: John Platt Source: [libSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets) - Date unknown Please cite: John C. Platt. Fast training of support vector machines using sequential minimal optimization. In Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, 1998. MIT Press.a This is the famous webdata dataset w[1-8]a in its binary version, retrieved 2014-11-14 from the libSVM site. Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows: * load all web data datasets, train and test, e.g. w1a, w1a.t, w2a, w2a.t, w3a, ... * join test and train for each subset, e.g. w1a and w1a.t, w2a and w2a.t * normalize each file columnwise according to the following rules: * If a column only contains one value (constant feature), it will set to zero and thus removed by sparsity. * If a column contains two values (binary feature), the value occuring more often will be set to zero, the other to one. * If a column contains more than two values (multinary/real feature), the column is divided by its std deviation. * afterwards all these 8 files are merged into one, and randomly sorted. * duplicate lines were finally removed. An R script which does all of these steps can be found here: https://github.com/openml/data_scripts/blob/master/webdata_wXa/dataDownloader.R

124 features

Y (target)nominal2 unique values
0 missing
X1numeric2 unique values
0 missing
X2numeric2 unique values
0 missing
X3numeric2 unique values
0 missing
X4numeric2 unique values
0 missing
X5numeric2 unique values
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X6numeric2 unique values
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X7numeric2 unique values
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X8numeric2 unique values
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X9numeric2 unique values
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X10numeric2 unique values
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X11numeric2 unique values
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X12numeric2 unique values
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X13numeric2 unique values
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X14numeric2 unique values
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X15numeric2 unique values
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X16numeric2 unique values
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X17numeric2 unique values
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X18numeric2 unique values
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X19numeric2 unique values
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X20numeric2 unique values
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X21numeric2 unique values
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X22numeric2 unique values
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X23numeric2 unique values
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X24numeric2 unique values
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X25numeric2 unique values
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X26numeric2 unique values
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X27numeric2 unique values
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X28numeric2 unique values
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X29numeric2 unique values
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X30numeric2 unique values
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X31numeric2 unique values
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X32numeric2 unique values
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X33numeric2 unique values
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X34numeric2 unique values
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X35numeric2 unique values
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X36numeric2 unique values
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X37numeric2 unique values
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X38numeric2 unique values
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X39numeric2 unique values
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X40numeric2 unique values
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X41numeric2 unique values
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X42numeric2 unique values
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X43numeric2 unique values
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X44numeric2 unique values
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X45numeric2 unique values
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X46numeric2 unique values
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X47numeric2 unique values
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X48numeric2 unique values
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X49numeric2 unique values
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X50numeric2 unique values
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X51numeric2 unique values
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X52numeric2 unique values
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X53numeric2 unique values
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X54numeric2 unique values
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X55numeric2 unique values
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X56numeric2 unique values
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X57numeric2 unique values
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X58numeric2 unique values
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X61numeric2 unique values
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X62numeric2 unique values
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X63numeric2 unique values
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X64numeric2 unique values
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X65numeric2 unique values
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X66numeric2 unique values
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X67numeric2 unique values
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X68numeric2 unique values
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X69numeric2 unique values
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X70numeric2 unique values
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X71numeric2 unique values
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X72numeric2 unique values
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X73numeric2 unique values
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X74numeric2 unique values
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X75numeric2 unique values
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X76numeric2 unique values
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X77numeric2 unique values
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X78numeric2 unique values
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X79numeric2 unique values
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X80numeric2 unique values
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X81numeric2 unique values
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X82numeric2 unique values
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X83numeric2 unique values
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X84numeric2 unique values
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X85numeric2 unique values
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X86numeric2 unique values
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X87numeric2 unique values
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X94numeric2 unique values
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X95numeric2 unique values
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X96numeric2 unique values
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X97numeric2 unique values
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X98numeric2 unique values
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X99numeric2 unique values
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X100numeric2 unique values
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X101numeric2 unique values
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X102numeric2 unique values
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X103numeric2 unique values
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X104numeric2 unique values
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X105numeric2 unique values
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X106numeric2 unique values
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X107numeric2 unique values
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X108numeric2 unique values
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X109numeric2 unique values
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X110numeric2 unique values
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X111numeric2 unique values
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X112numeric2 unique values
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X113numeric2 unique values
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X114numeric2 unique values
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X115numeric2 unique values
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X116numeric2 unique values
0 missing
X117numeric2 unique values
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X118numeric2 unique values
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X119numeric2 unique values
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X120numeric2 unique values
0 missing
X121numeric2 unique values
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X122numeric2 unique values
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X123numeric2 unique values
0 missing
Show first 100 features

107 properties

NumberOfInstances
36974
Number of instances (rows) of the dataset.
NumberOfFeatures
124
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
123
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.73
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.83
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
0.48
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
0.83
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
0.48
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
0.83
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
0.48
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
0.8
Entropy of the target attribute values.
DecisionStumpAUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
76
Percentage of instances belonging to the most frequent class.
MajorityClassSize
28100
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
36974
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.94
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
192.29
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.5
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
619.96
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.11
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
13.23
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.2
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.96
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
-3.8
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.01
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
24
Percentage of instances belonging to the least frequent class.
MinorityClassSize
8874
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
0.81
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.19
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.81
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
2.67
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
1.89
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.05
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
21.97
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.04
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
4.9
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
396.95
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.16
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
19.97
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.34
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.35
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
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

23 tasks

Supervised Classification on webdata_wXa
397 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Y
Supervised Classification on webdata_wXa
123 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Y
Learning Curve on webdata_wXa
44 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Y
Supervised Data Stream Classification on webdata_wXa
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Y
Clustering on webdata_wXa
0 runs
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Clustering on webdata_wXa
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
Subgroup Discovery on webdata_wXa
0 runs - target_feature: Y
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