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ringnorm

ringnorm

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Author: Michael Revow Source: http://www.cs.toronto.edu/~delve/data/ringnorm/desc.html Please cite: 1: Abstract: This is a 20 dimensional, 2 class classification problem. Each class is drawn from a multivariate normal distribution. Class 1 has mean zero and covariance 4 times the identity. Class 2 has mean (a,a,..a) and unit covariance. a = 2/sqrt(20). 2: Data set description. This is an implementation of Leo Breiman's ringnorm example[1]. It is a 20 dimensional, 2 class classification example. Each class is drawn from a multivariate normal distribution. Class 1 has mean zero and covariance 4 times the identity. Class 2 has mean (a,a,..a) and unit covariance. a = 2/sqrt(20). Breiman reports the theoretical expected misclassification rate as 1.3%. He used 300 training examples with CART and found an error of 21.4%. - Type. Classification - Origin. Laboratory - Instances. 7400 - Features. 20 - Classes. 2 - Missing values. No 3: Attributes information @relation ring @attribute A1 real [-6879.0, 6285.0] @attribute A2 real [-7141.0, 6921.0] @attribute A3 real [-7734.0, 7611.0] @attribute A4 real [-6627.0, 7149.0] @attribute A5 real [-7184.0, 6383.0] @attribute A6 real [-6946.0, 6743.0] @attribute A7 real [-7781.0, 6285.0] @attribute A8 real [-6882.0, 6357.0] @attribute A9 real [-7184.0, 7487.0] @attribute A10 real [-7232.0, 6757.0] @attribute A11 real [-7803.0, 7208.0] @attribute A12 real [-7395.0, 6791.0] @attribute A13 real [-7096.0, 6403.0] @attribute A14 real [-7472.0, 7261.0] @attribute A15 real [-7342.0, 7372.0] @attribute A16 real [-7121.0, 6905.0] @attribute A17 real [-7163.0, 7175.0] @attribute A18 real [-8778.0, 6896.0] @attribute A19 real [-7554.0, 5726.0] @attribute A20 real [-6722.0, 7627.0] @attribute Class {0, 1} @inputs A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20 @outputs Class

21 features

Class (target)nominal2 unique values
0 missing
V11numeric3786 unique values
0 missing
V20numeric3685 unique values
0 missing
V19numeric3742 unique values
0 missing
V18numeric3787 unique values
0 missing
V17numeric3725 unique values
0 missing
V16numeric3797 unique values
0 missing
V15numeric3745 unique values
0 missing
V14numeric3746 unique values
0 missing
V13numeric3751 unique values
0 missing
V12numeric3756 unique values
0 missing
V1numeric3739 unique values
0 missing
V10numeric3755 unique values
0 missing
V9numeric3765 unique values
0 missing
V8numeric3695 unique values
0 missing
V7numeric3764 unique values
0 missing
V6numeric3774 unique values
0 missing
V5numeric3760 unique values
0 missing
V4numeric3757 unique values
0 missing
V3numeric3807 unique values
0 missing
V2numeric3779 unique values
0 missing
Show all 21 features

107 properties

NumberOfInstances
7400
Number of instances (rows) of the dataset.
NumberOfFeatures
21
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
20
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.5
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.88
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
0.12
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
0.77
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
0.88
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
0.12
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
0.77
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
0.88
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
0.12
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
0.77
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.41
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.18
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
50.49
Percentage of instances belonging to the most frequent class.
MajorityClassSize
3736
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.71
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
225.82
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
-0.12
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1542.93
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1.51
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
204.23
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
-0.23
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1503.28
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
1.32
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
176.55
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
-0.31
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1471.26
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
49.51
Percentage of instances belonging to the least frequent class.
MinorityClassSize
3664
Number of instances belonging to the least frequent class.
NaiveBayesAUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
4.76
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
95.24
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
4.76
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
1.41
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
191.25
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
-0.26
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1485.84
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
1.51
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
201.98
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
-0.24
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1501.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.6
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
219.67
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
-0.22
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1518.98
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.77
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Classification on ringnorm
89 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on ringnorm
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on ringnorm
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Clustering on ringnorm
0 runs
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
Clustering on ringnorm
0 runs - estimation_procedure: 50 times Clustering
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