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analcatdata_boxing2

analcatdata_boxing2

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Author: Source: Unknown - Date unknown Please cite: analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two versions of each of 84 data sets, plus this README file. Each data set is given in comma-delimited ASCII (.csv) form, and Microsoft Excel (.xls) form. NOTICE: These data sets may be used freely for scientific, educational and/or noncommercial purposes, provided suitable acknowledgment is given (by citing the above-named reference). Further details concerning the book, including information on statistical software (including sample S-PLUS/R and SAS code), are available at the web site http://www.stern.nyu.edu/~jsimonof/AnalCatData Information about the dataset CLASSTYPE: nominal CLASSINDEX: last Note: Quotes, Single-Quotes and Backslashes were removed, Blanks replaced with Underscores

4 features

Winner (target)nominal2 unique values
0 missing
Judgenominal11 unique values
0 missing
Officialnominal2 unique values
0 missing
Roundnominal12 unique values
0 missing

107 properties

132
Number of instances (rows) of the dataset.
4
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.
0
Number of numeric attributes.
4
Number of nominal attributes.
15.94
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.6
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
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
6.75
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.84
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
5.5
Standard deviation of the number of distinct values among attributes of the nominal type.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.2
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
3.46
Second quartile (Median) of entropy among attributes.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.6
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
0.34
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
53.79
Percentage of instances belonging to the most frequent class.
0.85
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
71
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.58
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.42
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
50
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Number of attributes divided by the number of instances.
0.43
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
3.58
Third quartile of entropy among attributes.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
6.42
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
12
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.49
Average class difference between consecutive instances.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
46.21
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.84
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
61
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.43
Third quartile of mutual information between the nominal attributes and the target attribute.
0.2
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.63
Average entropy of the attributes.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.85
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.6
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
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.84
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.16
Average mutual information between the nominal attributes and the target attribute.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
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
0.2
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

5 tasks

817 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Winner
211 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Winner
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Winner
0 runs - estimation_procedure: 50 times Clustering
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