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analcatdata_authorship

analcatdata_authorship

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  • OpenML-CC18 OpenML100 study_1 study_123 study_135 study_14 study_34 study_41 study_50 study_52 study_7 study_98 study_99
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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

71 features

Author (target)nominal4 unique values
0 missing
anumeric57 unique values
0 missing
allnumeric27 unique values
0 missing
alsonumeric6 unique values
0 missing
annumeric50 unique values
0 missing
andnumeric83 unique values
0 missing
anynumeric16 unique values
0 missing
arenumeric21 unique values
0 missing
asnumeric31 unique values
0 missing
atnumeric22 unique values
0 missing
benumeric39 unique values
0 missing
beennumeric24 unique values
0 missing
butnumeric25 unique values
0 missing
bynumeric21 unique values
0 missing
cannumeric12 unique values
0 missing
donumeric22 unique values
0 missing
downnumeric13 unique values
0 missing
evennumeric9 unique values
0 missing
everynumeric12 unique values
0 missing
fornumeric27 unique values
0 missing
fromnumeric25 unique values
0 missing
hadnumeric45 unique values
0 missing
hasnumeric15 unique values
0 missing
havenumeric31 unique values
0 missing
hernumeric66 unique values
0 missing
hisnumeric54 unique values
0 missing
ifnumeric18 unique values
0 missing
innumeric45 unique values
0 missing
intonumeric15 unique values
0 missing
isnumeric40 unique values
0 missing
itnumeric49 unique values
0 missing
itsnumeric12 unique values
0 missing
maynumeric11 unique values
0 missing
morenumeric15 unique values
0 missing
mustnumeric16 unique values
0 missing
mynumeric51 unique values
0 missing
nonumeric23 unique values
0 missing
notnumeric37 unique values
0 missing
nownumeric16 unique values
0 missing
ofnumeric76 unique values
0 missing
onnumeric26 unique values
0 missing
onenumeric18 unique values
0 missing
onlynumeric11 unique values
0 missing
ornumeric29 unique values
0 missing
ournumeric30 unique values
0 missing
shouldnumeric14 unique values
0 missing
sonumeric24 unique values
0 missing
somenumeric13 unique values
0 missing
suchnumeric14 unique values
0 missing
thannumeric14 unique values
0 missing
thatnumeric38 unique values
0 missing
thenumeric137 unique values
0 missing
theirnumeric26 unique values
0 missing
thennumeric13 unique values
0 missing
therenumeric16 unique values
0 missing
thingsnumeric10 unique values
0 missing
thisnumeric27 unique values
0 missing
tonumeric63 unique values
0 missing
upnumeric14 unique values
0 missing
uponnumeric11 unique values
0 missing
wasnumeric64 unique values
0 missing
werenumeric31 unique values
0 missing
whatnumeric21 unique values
0 missing
whennumeric16 unique values
0 missing
whichnumeric20 unique values
0 missing
whonumeric16 unique values
0 missing
willnumeric25 unique values
0 missing
withnumeric36 unique values
0 missing
wouldnumeric23 unique values
0 missing
yournumeric31 unique values
0 missing
BookIDnumeric12 unique values
0 missing

107 properties

841
Number of instances (rows) of the dataset.
71
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.
70
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.88
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
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
4
Average number of distinct values among the attributes of the nominal type.
0.7
First quartile of skewness among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
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.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.11
Mean skewness among attributes of the numeric type.
2.52
First quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.08
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
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.36
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.88
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
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
37.69
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.41
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.79
Entropy of the target attribute values.
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
317
Number of instances belonging to the most frequent class.
-0.79
Minimum kurtosis among attributes of the numeric type.
5.03
Second quartile (Median) of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.44
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.44
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
21.23
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.03
Second quartile (Median) of skewness among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
77.36
Maximum of means among attributes of the numeric type.
4
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
3.9
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.08
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.04
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
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.
4
The maximum number of distinct values among attributes of the nominal type.
0.8
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
2.44
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.09
Maximum skewness among attributes of the numeric type.
6.54
Percentage of instances belonging to the least frequent class.
98.59
Percentage of numeric attributes.
11.94
Third quartile of means among attributes of the numeric type.
0.94
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
31.07
Maximum standard deviation of attributes of the numeric type.
55
Number of instances belonging to the least frequent class.
1.41
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.08
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
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.34
Third quartile of skewness among attributes of the numeric type.
0.88
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
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.36
Mean kurtosis among attributes of the numeric type.
0.01
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.36
First quartile of kurtosis among attributes of the numeric type.
6.21
Third quartile of standard deviation of attributes of the numeric type.
0.94
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
10.13
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.13
First quartile of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.08
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
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

25 tasks

13985 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Author
175 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Author
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Author
0 runs - estimation_procedure: 33% Holdout set - target_feature: Author
44 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Author
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Author
0 runs - target_feature: Author
1308 runs - target_feature: Author
1307 runs - target_feature: Author
1304 runs - target_feature: Author
1303 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
0 runs - target_feature: Author
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