Data
ringnorm

# ringnorm

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael Gomes Mantovani
• artificial study_52 study_7 study_236
Issue #Downvotes for this reason By

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) nominal 2 unique values 0 missing V1 numeric 3739 unique values 0 missing V2 numeric 3779 unique values 0 missing V3 numeric 3807 unique values 0 missing V4 numeric 3757 unique values 0 missing V5 numeric 3760 unique values 0 missing V6 numeric 3774 unique values 0 missing V7 numeric 3764 unique values 0 missing V8 numeric 3695 unique values 0 missing V9 numeric 3765 unique values 0 missing V10 numeric 3755 unique values 0 missing V11 numeric 3786 unique values 0 missing V12 numeric 3756 unique values 0 missing V13 numeric 3751 unique values 0 missing V14 numeric 3746 unique values 0 missing V15 numeric 3745 unique values 0 missing V16 numeric 3797 unique values 0 missing V17 numeric 3725 unique values 0 missing V18 numeric 3787 unique values 0 missing V19 numeric 3742 unique values 0 missing V20 numeric 3685 unique values 0 missing

### 107 properties

7400
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
20
Number of numeric attributes.
1
Number of nominal attributes.
1.32
Minimum kurtosis among attributes of the numeric type.
201.98
Second quartile (Median) of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
176.55
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.41
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.71
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0.24
Second quartile (Median) of skewness among attributes of the numeric type.
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
225.82
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
4.76
Percentage of binary attributes.
1501.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.31
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
1471.26
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1.6
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-0.12
Maximum skewness among attributes of the numeric type.
49.51
Percentage of instances belonging to the least frequent class.
95.24
Percentage of numeric attributes.
219.67
Third quartile of means among attributes of the numeric type.
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1542.93
Maximum standard deviation of attributes of the numeric type.
3664
Number of instances belonging to the least frequent class.
4.76
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
-0.22
Third quartile of skewness among attributes of the numeric type.
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
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.51
Mean kurtosis among attributes of the numeric type.
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.41
First quartile of kurtosis among attributes of the numeric type.
1518.98
Third quartile of standard deviation of attributes of the numeric type.
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
204.23
Mean of means among attributes of the numeric type.
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
191.25
First quartile of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Average number of distinct values among the attributes of the nominal type.
-0.26
First quartile of skewness among attributes of the numeric type.
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.23
Mean skewness among attributes of the numeric type.
1485.84
First quartile of standard deviation of attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1503.28
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.26
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.49
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.51
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
3736
Number of instances belonging to the most frequent class.

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