Data
vowel

vowel

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Author: Peter Turney (peter@ai.iit.nrc.ca) Source: [UCI](https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/vowel/) - date unknown Please cite: Vowel Recognition Data In my work on context-sensitive learning, I used the "Deterding Vowel Recognition Data", but I found it necessary to reformulate the data. Implicit in the original data is contextual information on the speaker's gender and identity. For my work, it was necessary to make this information explicit. This dataset adds the speaker's sex and identity as new features. Notes: * This is version 2. Version 1 is hidden because it includes a feature dividing the data in train and test set. In OpenML this information is explicitly available in the corresponding task.

13 features

Class (target)nominal11 unique values
0 missing
Speaker_Numbernominal15 unique values
0 missing
Sexnominal2 unique values
0 missing
Feature_0numeric853 unique values
0 missing
Feature_1numeric877 unique values
0 missing
Feature_2numeric815 unique values
0 missing
Feature_3numeric836 unique values
0 missing
Feature_4numeric803 unique values
0 missing
Feature_5numeric798 unique values
0 missing
Feature_6numeric748 unique values
0 missing
Feature_7numeric794 unique values
0 missing
Feature_8numeric788 unique values
0 missing
Feature_9numeric775 unique values
0 missing

107 properties

990
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
11
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.
10
Number of numeric attributes.
3
Number of nominal attributes.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.86
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
6.66
Standard deviation of the number of distinct values among attributes of the nominal type.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
9.33
Average number of distinct values among the attributes of the nominal type.
-0.01
First quartile of skewness among attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.31
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.09
Mean skewness among attributes of the numeric type.
0.57
First quartile of standard deviation of attributes of the numeric type.
0.64
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.66
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
0.08
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
9.09
Percentage of instances belonging to the most frequent class.
0.7
Mean standard deviation of attributes of the numeric type.
2.45
Second quartile (Median) of entropy among attributes.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.46
Entropy of the target attribute values.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
90
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
-0.43
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.84
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
3.91
Maximum entropy among attributes.
-0.76
Minimum kurtosis among attributes of the numeric type.
-0.04
Second quartile (Median) of means among attributes of the numeric type.
0.64
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.82
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.15
Maximum kurtosis among attributes of the numeric type.
-3.2
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1.88
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.06
Second quartile (Median) of skewness among attributes of the numeric type.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
0
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
7.69
Percentage of binary attributes.
0.63
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.29
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.
15
The maximum number of distinct values among attributes of the nominal type.
-0.21
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
3.91
Third quartile of entropy among attributes.
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.36
Maximum skewness among attributes of the numeric type.
0.46
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.24
Third quartile of kurtosis among attributes of the numeric type.
0
Average class difference between consecutive instances.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.18
Maximum standard deviation of attributes of the numeric type.
9.09
Percentage of instances belonging to the least frequent class.
76.92
Percentage of numeric attributes.
0.54
Third quartile of means among attributes of the numeric type.
0.86
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.45
Average entropy of the attributes.
90
Number of instances belonging to the least frequent class.
23.08
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
0.31
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
0.66
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.39
Mean kurtosis among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
First quartile of entropy among attributes.
0.25
Third quartile of skewness among attributes of the numeric type.
0.86
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
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.1
Mean of means among attributes of the numeric type.
0.42
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.56
First quartile of kurtosis among attributes of the numeric type.
0.79
Third quartile of standard deviation of attributes of the numeric type.
0.31
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Average mutual information between the nominal attributes and the target attribute.
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.36
First quartile of means among attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.64
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.66
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
0.87
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.
0
First quartile of mutual information between the nominal attributes and the target attribute.

11 tasks

0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Define a new task