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analcatdata_germangss

analcatdata_germangss

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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: 1 Note: Quotes, Single-Quotes and Backslashes were removed, Blanks replaced with Underscores

6 features

Political_system (target)nominal4 unique values
0 missing
Agenominal5 unique values
0 missing
Time_of_surveynominal2 unique values
0 missing
Schoolingnominal5 unique values
0 missing
Regionnominal2 unique values
0 missing
Countnumeric73 unique values
0 missing

107 properties

400
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
4
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.
1
Number of numeric attributes.
5
Number of nominal attributes.
0.21
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
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
11.94
Mean kurtosis among attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
First quartile of entropy among attributes.
3.22
Third quartile of skewness among attributes of the numeric type.
0.7
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
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.64
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
15.1
Mean of means among attributes of the numeric type.
0.67
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
11.94
First quartile of kurtosis among attributes of the numeric type.
30.03
Third quartile of standard deviation of attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.59
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
0.74
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Average mutual information between the nominal attributes and the target attribute.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
15.1
First quartile of means among attributes of the numeric type.
0.69
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.21
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
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.64
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
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.7
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
1.52
Standard deviation of the number of distinct values among attributes of the nominal type.
0.64
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.6
Average number of distinct values among the attributes of the nominal type.
3.22
First quartile of skewness among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.59
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
0.36
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.22
Mean skewness among attributes of the numeric type.
30.03
First quartile of standard deviation of attributes of the numeric type.
0.69
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.21
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
0.93
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
25
Percentage of instances belonging to the most frequent class.
30.03
Mean standard deviation of attributes of the numeric type.
1.66
Second quartile (Median) of entropy among attributes.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2
Entropy of the target attribute values.
-0.24
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
100
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
11.94
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.32
Maximum entropy among attributes.
11.94
Minimum kurtosis among attributes of the numeric type.
15.1
Second quartile (Median) of means among attributes of the numeric type.
0.69
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.62
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
11.94
Maximum kurtosis among attributes of the numeric type.
15.1
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
15.1
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
3.22
Second quartile (Median) of skewness among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
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.
33.33
Percentage of binary attributes.
30.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.74
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.
5
The maximum number of distinct values among attributes of the nominal type.
3.22
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2.32
Third quartile of entropy among attributes.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.22
Maximum skewness among attributes of the numeric type.
30.03
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
11.94
Third quartile of kurtosis among attributes of the numeric type.
0.9
Average class difference between consecutive instances.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.64
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
30.03
Maximum standard deviation of attributes of the numeric type.
25
Percentage of instances belonging to the least frequent class.
16.67
Percentage of numeric attributes.
15.1
Third quartile of means among attributes of the numeric type.
0.7
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
0.59
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
0.74
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.66
Average entropy of the attributes.
100
Number of instances belonging to the least frequent class.
83.33
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.

5 tasks

393 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Political_system
188 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Political_system
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Political_system
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