Data
collins

collins

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: The following are data used in an analysis of the Brown and Frown corpora for my doctoral dissertation titled ``Variations in Written English: Characterizing Authors' Rhetorical Language Choices Across Corpora of Published Texts" (Completed at Carnegie Mellon Univ, 2003). The source of the corpora was the ICAME CD-ROM (get info at ). The data were generated from the texts using tagging and visualization software, Docuscope. The first row is the variable names. The genre of each text (assigned by the Brown corpus compilers) is in 'Genre' column and the corpus is listed in the 'corpus' column with 1=Brown and 2=Frown corpus. The dataset may be freely used and distributed for non-commercial purposes. Jeff Collins 11 July 2003 Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

22 features

Corp.Genre (target)nominal15 unique values
0 missing
LinearGuidancenumeric352 unique values
0 missing
Corpusnominal1 unique values
0 missing
Counter (row identifier)numeric500 unique values
0 missing
Genrenominal15 unique values
0 missing
Text_Coveragenumeric443 unique values
0 missing
ShiftingEventsnumeric132 unique values
0 missing
TimeIntervalnumeric173 unique values
0 missing
PastEventsnumeric283 unique values
0 missing
Motionnumeric123 unique values
0 missing
SpaceIntervalnumeric228 unique values
0 missing
WordPicturenumeric371 unique values
0 missing
Text (ignore)nominal500 unique values
0 missing
Notifyingnumeric218 unique values
0 missing
Interactingnumeric160 unique values
0 missing
Direct_Activitynumeric78 unique values
0 missing
Share_SocTiesnumeric260 unique values
0 missing
Reasoningnumeric262 unique values
0 missing
ThinkBacknumeric130 unique values
0 missing
ThinkAheadnumeric172 unique values
0 missing
ThinkNegativenumeric216 unique values
0 missing
ThinkPositivenumeric154 unique values
0 missing
InnerThinkingnumeric262 unique values
0 missing
FirstPersonnumeric139 unique values
0 missing

107 properties

500
Number of instances (rows) of the dataset.
22
Number of attributes (columns) of the dataset.
15
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.
19
Number of numeric attributes.
3
Number of nominal attributes.
0.97
Average class difference between consecutive instances.
1
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
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
1
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
1
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
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
1
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
1
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
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
1
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
3.65
Entropy of the target attribute values.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.74
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.04
Number of attributes divided by the number of instances.
2
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
16
Percentage of instances belonging to the most frequent class.
80
Number of instances belonging to the most frequent class.
3.65
Maximum entropy among attributes.
29.62
Maximum kurtosis among attributes of the numeric type.
31.49
Maximum of means among attributes of the numeric type.
3.65
Maximum mutual information between the nominal attributes and the target attribute.
15
The maximum number of distinct values among attributes of the nominal type.
4.22
Maximum skewness among attributes of the numeric type.
5.21
Maximum standard deviation of attributes of the numeric type.
1.82
Average entropy of the attributes.
4.03
Mean kurtosis among attributes of the numeric type.
3.31
Mean of means among attributes of the numeric type.
1.82
Average mutual information between the nominal attributes and the target attribute.
-0
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
10.33
Average number of distinct values among the attributes of the nominal type.
1.22
Mean skewness among attributes of the numeric type.
1.05
Mean standard deviation of attributes of the numeric type.
-0
Minimal entropy among attributes.
-0.57
Minimum kurtosis among attributes of the numeric type.
0.28
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
-0.07
Minimum skewness among attributes of the numeric type.
0.27
Minimum standard deviation of attributes of the numeric type.
1.2
Percentage of instances belonging to the least frequent class.
6
Number of instances belonging to the least frequent class.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.36
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
86.36
Percentage of numeric attributes.
13.64
Percentage of nominal attributes.
-0
First quartile of entropy among attributes.
0.31
First quartile of kurtosis among attributes of the numeric type.
0.63
First quartile of means among attributes of the numeric type.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.54
First quartile of skewness among attributes of the numeric type.
0.41
First quartile of standard deviation of attributes of the numeric type.
1.82
Second quartile (Median) of entropy among attributes.
0.76
Second quartile (Median) of kurtosis among attributes of the numeric type.
1.32
Second quartile (Median) of means among attributes of the numeric type.
1.82
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.87
Second quartile (Median) of skewness among attributes of the numeric type.
0.77
Second quartile (Median) of standard deviation of attributes of the numeric type.
3.65
Third quartile of entropy among attributes.
3.7
Third quartile of kurtosis among attributes of the numeric type.
2.67
Third quartile of means among attributes of the numeric type.
3.65
Third quartile of mutual information between the nominal attributes and the target attribute.
1.61
Third quartile of skewness among attributes of the numeric type.
1.01
Third quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
8.08
Standard deviation of the number of distinct values among attributes of the nominal type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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