Data
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
V1numeric3739 unique values
0 missing
V2numeric3779 unique values
0 missing
V3numeric3807 unique values
0 missing
V4numeric3757 unique values
0 missing
V5numeric3760 unique values
0 missing
V6numeric3774 unique values
0 missing
V7numeric3764 unique values
0 missing
V8numeric3695 unique values
0 missing
V9numeric3765 unique values
0 missing
V10numeric3755 unique values
0 missing
V11numeric3786 unique values
0 missing
V12numeric3756 unique values
0 missing
V13numeric3751 unique values
0 missing
V14numeric3746 unique values
0 missing
V15numeric3745 unique values
0 missing
V16numeric3797 unique values
0 missing
V17numeric3725 unique values
0 missing
V18numeric3787 unique values
0 missing
V19numeric3742 unique values
0 missing
V20numeric3685 unique values
0 missing

62 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.
4.76
Percentage of binary attributes.
1501.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
1.32
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1.71
Maximum kurtosis among attributes of the numeric type.
176.55
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
1.6
Third quartile of kurtosis among attributes of the numeric type.
225.82
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
95.24
Percentage of numeric attributes.
219.67
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
4.76
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.31
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
-0.22
Third quartile of skewness among attributes of the numeric type.
-0.12
Maximum skewness among attributes of the numeric type.
1471.26
Minimum standard deviation of attributes of the numeric type.
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.
1542.93
Maximum standard deviation of attributes of the numeric type.
49.51
Percentage of instances belonging to the least frequent class.
191.25
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
3664
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
1.51
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
-0.26
First quartile of skewness among attributes of the numeric type.
204.23
Mean of means among attributes of the numeric type.
1485.84
First quartile of standard deviation of attributes of the numeric type.
0.5
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
1
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.51
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
201.98
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-0.23
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
50.49
Percentage of instances belonging to the most frequent class.
1503.28
Mean standard deviation of attributes of the numeric type.
-0.24
Second quartile (Median) of skewness among attributes of the numeric type.
3736
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

6 tasks

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
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