Data
socmob

socmob

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized_regression_problem mythbusting_1 study_1 study_144 study_15 study_20 study_41 study_7
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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').

6 features

binaryClass (target)nominal2 unique values
0 missing
fathers_occupationnominal17 unique values
0 missing
sons_occupationnominal17 unique values
0 missing
family_structurenominal2 unique values
0 missing
racenominal2 unique values
0 missing
counts_for_sons_first_occupationnumeric358 unique values
0 missing

107 properties

1156
Number of instances (rows) of the dataset.
6
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.
1
Number of numeric attributes.
5
Number of nominal attributes.
44.36
First quartile of standard deviation of attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.07
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
7.19
Mean skewness among attributes of the numeric type.
2.54
Second quartile (Median) of entropy among attributes.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.79
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
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
77.85
Percentage of instances belonging to the most frequent class.
44.36
Mean standard deviation of attributes of the numeric type.
81.88
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.76
Entropy of the target attribute values.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
900
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
16.94
Second quartile (Median) of means among attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4.09
Maximum entropy among attributes.
81.88
Minimum kurtosis among attributes of the numeric type.
0.08
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
81.88
Maximum kurtosis among attributes of the numeric type.
16.94
Minimum of means among attributes of the numeric type.
7.19
Second quartile (Median) of skewness among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
16.94
Maximum of means among attributes of the numeric type.
0.04
Minimal mutual information between the nominal attributes and the target attribute.
44.36
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.9
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.
0.17
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
50
Percentage of binary attributes.
4.09
Third quartile of entropy among attributes.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
8.19
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
17
The maximum number of distinct values among attributes of the nominal type.
7.19
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
81.88
Third quartile of kurtosis among attributes of the numeric type.
0.89
Average class difference between consecutive instances.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
7.19
Maximum skewness among attributes of the numeric type.
44.36
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
16.94
Third quartile of means among attributes of the numeric type.
0.91
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
0.9
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
44.36
Maximum standard deviation of attributes of the numeric type.
22.15
Percentage of instances belonging to the least frequent class.
16.67
Percentage of numeric attributes.
0.16
Third quartile of mutual information between the nominal attributes and the target attribute.
0.07
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
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.54
Average entropy of the attributes.
256
Number of instances belonging to the least frequent class.
83.33
Percentage of nominal attributes.
7.19
Third quartile of skewness among attributes of the numeric type.
0.79
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
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
81.88
Mean kurtosis among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
First quartile of entropy among attributes.
44.36
Third quartile of standard deviation of attributes of the numeric type.
0.91
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
0.9
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
16.94
Mean of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
81.88
First quartile of kurtosis among attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.07
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
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.09
Average mutual information between the nominal attributes and the target attribute.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
16.94
First quartile of means among attributes of the numeric type.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.79
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
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
26.3
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3
Number of binary attributes.
0.04
First quartile of mutual information between the nominal attributes and the target attribute.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.91
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
8.22
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
8
Average number of distinct values among the attributes of the nominal type.
7.19
First quartile of skewness among attributes of the numeric type.

7 tasks

527 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
233 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - 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
0 runs - estimation_procedure: 50 times Clustering
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