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Author: Source: Unknown - Date unknown Please cite: Irish Educational Transitions Data Below are shown data on educational transitions for a sample of 500 Irish schoolchildren aged 11 in 1967. The data were collected by Greaney and Kelleghan (1984), and reanalyzed by Raftery and Hout (1985, 1993). The data were also used, in a simplified form, as an example to illustrate Bayesian model selection methods by Raftery (1988) and Kass and Raftery (1993). In that simplified form, primary terminal leavers and cases with any missing data were removed, leaving 441 cases. The Leaving Certificate variable was used as the dependent variable in a logistic regression analysis. The variables shown are as follows: 1. Sex: 1=male; 2=female. 2. DVRT (Drumcondra Verbal Reasoning Test Score). 3. Educational level attained: 1 Primary terminal leaver 2 Junior cycle incomplete: vocational school 3 Junior cycle incomplete: secondary school 4 Junior cycle terminal leaver: vocational school 5 Junior cycle terminal leaver: secondary school 6 Senior cycle incomplete: vocational school 7 Senior cycle incomplete: secondary school 8 Senior cycle terminal leaver: vocational school 9 Senior cycle terminal leaver: secondary school 10 3rd level incomplete 11 3rd level complete 4. Leaving Certificate. 1 if Leaving Certificate not taken; 2 if taken. 5. Prestige score for father's occupation (calculated by Raftery and Hout, 1985). 0 if missing. 6. Type of school: 1=secondary; 2=vocational; 9=primary terminal leaver. REFERENCES Greaney, V. and Kelleghan, T. (1984). Equality of Opportunity in Irish Schools. Dublin: Educational Company. Kass, R.E. and Raftery, A.E. (1993). Bayes factors and model uncertainty. Technical Report no. 254, Department of Statistics, University of Washington. Revised version to appear in Journal of the American Statistical Association. Raftery, A.E. (1988). Approximate Bayes factors for generalized linear models. Technical Report no. 121, Department of Statistics, University of Washington. Raftery, A.E. and Hout, M. (1985). Does Irish education approach the meritocratic ideal? A logistic analysis. Economic and Social Review, 16, 115-140. Raftery, A.E. and Hout, M. (1993). Maximally maintained inequality: Expansion, reform and opportunity in Irish schools. Sociology of Education, 66, 41-62. OWNERSHIP STATEMENT This data belongs to Vincent Greaney and Thomas Kelleghan, Educational Research Centre, St. Patrick's College, Drumcondra, Dublin 9, Ireland, who retain the copyright. In the form given here, it may be used solely as an example for research on the development of statistical methods. For any other use of the data, permission must be obtained from the owners. Subject to this statement, permission is hereby given to StatLib to distribute this data freely. Submitted by Adrian Raftery (raftery@stat.washington.edu). Copyright 1984 Vincent Greaney and Thomas Kelleghan. Information about the dataset CLASSTYPE: nominal CLASSINDEX: 4

6 features

Leaving_Certificate (target)nominal2 unique values
0 missing
Sexnominal2 unique values
0 missing
DVRTnumeric68 unique values
0 missing
Educational_levelnominal10 unique values
6 missing
Prestige_scorenumeric28 unique values
26 missing
Type_schoolnominal3 unique values
0 missing

107 properties

500
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).
32
Number of missing values in the dataset.
32
Number of instances with at least one value missing.
2
Number of numeric attributes.
4
Number of nominal attributes.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.05
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
First quartile of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
3.86
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.25
Average number of distinct values among the attributes of the nominal type.
-0.08
First quartile of skewness among attributes of the numeric type.
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.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.18
Mean skewness among attributes of the numeric type.
15.33
First 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 2
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
0.02
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.6
Percentage of instances belonging to the most frequent class.
15.4
Mean standard deviation of attributes of the numeric type.
1.19
Second quartile (Median) of entropy among attributes.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
278
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
-0.45
Second quartile (Median) of kurtosis among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
2.86
Maximum entropy among attributes.
-0.5
Minimum kurtosis among attributes of the numeric type.
69.54
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.4
Maximum kurtosis among attributes of the numeric type.
38.93
Minimum of means among attributes of the numeric type.
0.31
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.13
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
100.15
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.18
Second quartile (Median) of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.93
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
33.33
Percentage of binary attributes.
15.4
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.96
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.
10
The maximum number of distinct values among attributes of the nominal type.
-0.08
Minimum skewness among attributes of the numeric type.
6.4
Percentage of instances having missing values.
2.86
Third quartile of entropy among attributes.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
2.39
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.45
Maximum skewness among attributes of the numeric type.
15.33
Minimum standard deviation of attributes of the numeric type.
1.07
Percentage of missing values.
-0.4
Third quartile of kurtosis among attributes of the numeric type.
0.61
Average class difference between consecutive instances.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
15.46
Maximum standard deviation of attributes of the numeric type.
44.4
Percentage of instances belonging to the least frequent class.
33.33
Percentage of numeric attributes.
100.15
Third quartile of means among attributes of the numeric type.
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
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
1.68
Average entropy of the attributes.
222
Number of instances belonging to the least frequent class.
66.67
Percentage of nominal attributes.
0.93
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.45
Mean kurtosis among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
First quartile of entropy among attributes.
0.45
Third quartile of skewness among attributes of the numeric type.
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
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
69.54
Mean of means among attributes of the numeric type.
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.5
First quartile of kurtosis among attributes of the numeric type.
15.46
Third quartile of standard deviation of attributes of the numeric type.
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.42
Average mutual information between the nominal attributes and the target attribute.
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
38.93
First quartile of means among 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.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

11 tasks

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