Data
twonorm

twonorm

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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Author: Michael Revow Source: http://www.cs.toronto.edu/~delve/data/twonorm/desc.html Please cite: * Twonorm dataset This is an implementation of Leo Breiman's twonorm example[1]. It is a 20 dimensional, 2 class classification example. Each class is drawn from a multivariate normal distribution with unit variance. Class 1 has mean (a,a,..a) while Class 2 has mean (-a,-a,..-a). Where a = 2/sqrt(20). Breiman reports the theoretical expected misclassification rate as 2.3%. He used 300 training examples with CART and found an error of 22.1%.

21 features

Class (target)nominal2 unique values
0 missing
V1numeric6748 unique values
0 missing
V2numeric6734 unique values
0 missing
V3numeric6768 unique values
0 missing
V4numeric6735 unique values
0 missing
V5numeric6787 unique values
0 missing
V6numeric6743 unique values
0 missing
V7numeric6771 unique values
0 missing
V8numeric6776 unique values
0 missing
V9numeric6742 unique values
0 missing
V10numeric6790 unique values
0 missing
V11numeric6764 unique values
0 missing
V12numeric6789 unique values
0 missing
V13numeric6731 unique values
0 missing
V14numeric6760 unique values
0 missing
V15numeric6732 unique values
0 missing
V16numeric6740 unique values
0 missing
V17numeric6775 unique values
0 missing
V18numeric6732 unique values
0 missing
V19numeric6706 unique values
0 missing
V20numeric6762 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.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.14
Minimum kurtosis among attributes of the numeric type.
0.01
Second quartile (Median) of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.05
Maximum kurtosis among attributes of the numeric type.
-0.02
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.01
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Second quartile (Median) of skewness among attributes of the numeric type.
0.84
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.
2
The minimal number of distinct values among attributes of the nominal type.
4.76
Percentage of binary attributes.
1.09
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.16
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.
-0.03
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.05
Maximum skewness among attributes of the numeric type.
1.08
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.03
Third quartile of kurtosis among attributes of the numeric type.
0.49
Average class difference between consecutive instances.
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.11
Maximum standard deviation of attributes of the numeric type.
49.96
Percentage of instances belonging to the least frequent class.
95.24
Percentage of numeric attributes.
0.01
Third quartile of means among attributes of the numeric type.
0.16
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
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
3697
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.
0.68
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
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.06
Mean kurtosis among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.04
Third quartile of skewness among attributes of the numeric type.
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Mean of means among attributes of the numeric type.
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.09
First quartile of kurtosis among attributes of the numeric type.
1.1
Third quartile of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
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
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.01
First quartile of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.68
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
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.01
First quartile of skewness among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.01
Mean skewness among attributes of the numeric type.
1.09
First quartile of standard deviation of attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.68
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
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.04
Percentage of instances belonging to the most frequent class.
1.09
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
3703
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.07
Second quartile (Median) of kurtosis among attributes of the numeric type.

7 tasks

87 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
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