Data
analcatdata_dmft

analcatdata_dmft

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
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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: Unknown Source: [Jeffrey S. Simonoff](http://people.stern.nyu.edu/jsimonof/AnalCatData/Data/) - 2003 Please cite: Jeffrey S. Simonoff, Analyzing Categorical Data, Springer-Verlag, 2003 One of the datasets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff. It contains data on the DMFT Index (Decayed, Missing, and Filled Teeth) before and after different prevention strategies. The prevention strategy is commonly used as the (categorical) target. ### Attribute information * DMFT.Begin and DMFT.End: DMFT index before and after the prevention strategy * Gender of the individual * Ethnicity of the individual

5 features

Prevention (target)nominal6 unique values
0 missing
DMFT.Beginnominal9 unique values
0 missing
DMFT.Endnominal7 unique values
0 missing
Gendernominal2 unique values
0 missing
Ethnicnominal3 unique values
0 missing

107 properties

797
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
6
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.
5
Number of nominal attributes.
47.17
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.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.82
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
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
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.4
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.53
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
2.88
Standard deviation of the number of distinct values among attributes of the nominal type.
0.81
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.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.81
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
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
1.99
Second quartile (Median) of entropy among attributes.
0.82
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
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
0.81
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
19.45
Percentage of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.58
Entropy of the target attribute values.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
155
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.
0.53
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
3.08
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.82
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.8
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.04
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.
20
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.52
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.
0.06
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.
2.95
Third quartile of entropy among attributes.
0.8
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
61.62
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
9
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.
0.99
Average class difference between consecutive instances.
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
15.43
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.53
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
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.81
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
123
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
0.81
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
0.8
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.02
Average entropy of the attributes.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.11
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.03
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
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.77
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.
0.53
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
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.81
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.04
Average mutual information between the nominal attributes and the target attribute.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.81
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
0.8
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

33 tasks

14104 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Prevention
183 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Prevention
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Prevention
0 runs - estimation_procedure: 33% Holdout set - target_feature: Prevention
46 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Prevention
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Prevention
0 runs - target_feature: Prevention
1314 runs - target_feature: Prevention
1310 runs - target_feature: Prevention
1308 runs - target_feature: Prevention
1304 runs - target_feature: Prevention
1302 runs - target_feature: Prevention
1300 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
0 runs - target_feature: Prevention
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