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hutsof99_child_witness

hutsof99_child_witness

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized mythbusting_1 study_1 study_123 study_15 study_20
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

17 features

binaryClass (target)nominal2 unique values
0 missing
subj (row identifier)numeric42 unique values
0 missing
Q1numeric17 unique values
0 missing
Q2numeric22 unique values
0 missing
Q3numeric23 unique values
0 missing
Q4numeric21 unique values
0 missing
Q5numeric18 unique values
0 missing
Q6numeric18 unique values
0 missing
Q7numeric18 unique values
0 missing
Q8numeric15 unique values
0 missing
Q9numeric18 unique values
0 missing
Q10numeric17 unique values
0 missing
Q11numeric22 unique values
0 missing
Q12numeric21 unique values
0 missing
Q13numeric21 unique values
0 missing
Q14numeric21 unique values
0 missing
compnumeric39 unique values
0 missing

107 properties

42
Number of instances (rows) of the dataset.
17
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.
16
Number of numeric attributes.
1
Number of nominal attributes.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.29
Minimum kurtosis among attributes of the numeric type.
3.08
Second quartile (Median) of means among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.09
Maximum kurtosis among attributes of the numeric type.
1.98
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
41.83
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0.09
Second quartile (Median) of skewness among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.4
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
5.88
Percentage of binary attributes.
0.78
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.29
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.
2
The maximum number of distinct values among attributes of the nominal type.
-0.42
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
-0.31
Third quartile of kurtosis among attributes of the numeric type.
0.66
Average class difference between consecutive instances.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.64
Maximum skewness among attributes of the numeric type.
0.63
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
3.37
Third quartile of means among attributes of the numeric type.
0.65
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
20.66
Maximum standard deviation of attributes of the numeric type.
40.48
Percentage of instances belonging to the least frequent class.
94.12
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.38
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
17
Number of instances belonging to the least frequent class.
5.88
Percentage of nominal attributes.
0.1
Third quartile of skewness among attributes of the numeric type.
0.26
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
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.58
Mean kurtosis among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.84
Third quartile of standard deviation of attributes of the numeric type.
0.65
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.5
Mean of means among attributes of the numeric type.
0.19
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.88
First quartile of kurtosis among attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.38
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.54
First quartile of means among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.26
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
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.73
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.65
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.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.26
First quartile of skewness among attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.38
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.03
Mean skewness among attributes of the numeric type.
0.68
First quartile of standard deviation of attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.26
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
0.21
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
59.52
Percentage of instances belonging to the most frequent class.
2.1
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Entropy of the target attribute values.
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
25
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.53
Second quartile (Median) of kurtosis among attributes of the numeric type.

5 tasks

112 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
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