Data
PopularKids

PopularKids

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Datasets of Data And Story Library, project illustrating use of basic statistic methods, converted to arff format by Hakan Kjellerstrand. Source: TunedIT: http://tunedit.org/repo/DASL DASL file http://lib.stat.cmu.edu/DASL/Datafiles/PopularKids.html Students' Goals , What Makes Kids Popular Reference: Chase, M. A., and Dummer, G. M. (1992), "The Role of Sports as a Social Determinant for Children," Research Quarterly for Exercise and Sport, 63, 418-424 Authorization: Contact authors Description: Subjects were students in grades 4-6 from three school districts in Ingham and Clinton Counties, Michigan. Chase and Dummer stratified their sample, selecting students from urban, suburban, and rural school districts with approximately 1/3 of their sample coming from each district. Students indicated whether good grades, athletic ability, or popularity was most important to them. They also ranked four factors: grades, sports, looks, and money, in order of their importance for popularity. The questionnaire also asked for gender, grade level, and other demographic information. Number of cases: 478 Variable Names: Gender: Boy or girl Grade: 4, 5 or 6 Age: Age in years Race: White, Other Urban/Rural: Rural, Suburban, or Urban school district School: Brentwood Elementary, Brentwood Middle, Ridge, Sand, Eureka, Brown, Main, Portage, Westdale Middle Goals: Student's choice in the personal goals question where options were 1 = Make Good Grades, 2 = Be Popular, 3 = Be Good in Sports Grades: Rank of "make good grades" (1=most important for popularity, 4=least important) Sports: Rank of "being good at sports" (1=most important for popularity, 4=least important) Looks: Rank of "being handsome or pretty" (1=most important for popularity, 4=least important) Money: Rank of "having lots of money" (1=most important for popularity, 4=least important)

11 features

Goals (target)nominal3 unique values
0 missing
Gendernominal2 unique values
0 missing
Gradenumeric3 unique values
0 missing
Agenumeric6 unique values
0 missing
Racenominal2 unique values
0 missing
Urban/Ruralnominal3 unique values
0 missing
Schoolnominal9 unique values
0 missing
Gradesnumeric4 unique values
0 missing
Sportsnumeric4 unique values
0 missing
Looksnumeric4 unique values
0 missing
Moneynumeric4 unique values
0 missing

107 properties

478
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
3
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.
6
Number of numeric attributes.
5
Number of nominal attributes.
9
The maximum number of distinct values among attributes of the nominal type.
-0.9
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2.7
Third quartile of entropy among attributes.
0.53
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
50.14
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.48
Maximum skewness among attributes of the numeric type.
0.78
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.48
Third quartile of kurtosis among attributes of the numeric type.
0.43
Average class difference between consecutive instances.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.08
Maximum standard deviation of attributes of the numeric type.
18.83
Percentage of instances belonging to the least frequent class.
54.55
Percentage of numeric attributes.
6.46
Third quartile of means among attributes of the numeric type.
0.56
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.51
Average entropy of the attributes.
90
Number of instances belonging to the least frequent class.
45.45
Percentage of nominal attributes.
0.05
Third quartile of mutual information between the nominal attributes and the target attribute.
0.46
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
0.53
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-0.9
Mean kurtosis among attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.54
First quartile of entropy among attributes.
0.46
Third quartile of skewness among attributes of the numeric type.
0.16
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
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4.26
Mean of means among attributes of the numeric type.
0.5
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.28
First quartile of kurtosis among attributes of the numeric type.
1.07
Third quartile of standard deviation of attributes of the numeric type.
0.56
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.03
Average mutual information between the nominal attributes and the target attribute.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.1
First quartile of means among attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.46
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
0.53
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
50.65
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.47
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
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
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.8
Average number of distinct values among the attributes of the nominal type.
-0.41
First quartile of skewness among attributes of the numeric type.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.56
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
2.95
Standard deviation of the number of distinct values among attributes of the nominal type.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.07
Mean skewness among attributes of the numeric type.
0.91
First quartile of standard deviation of attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.46
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.97
Mean standard deviation of attributes of the numeric type.
1.29
Second quartile (Median) of entropy among attributes.
0.47
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
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
0.51
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
51.67
Percentage of instances belonging to the most frequent class.
0.39
Minimal entropy among attributes.
-0.98
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.47
Entropy of the target attribute values.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
247
Number of instances belonging to the most frequent class.
-1.34
Minimum kurtosis among attributes of the numeric type.
2.91
Second quartile (Median) of means among attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.07
Maximum entropy among attributes.
2.09
Minimum of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.47
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.52
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.28
Maximum kurtosis among attributes of the numeric type.
10.42
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
-0.1
Second quartile (Median) of skewness among attributes of the numeric type.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.05
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
18.18
Percentage of binary attributes.
0.98
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.

6 tasks

405 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Goals
188 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Goals
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Goals
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task