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cmc

cmc

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title: Contraceptive Method Choice 2. Sources: (a) Origin: This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey (b) Creator: Tjen-Sien Lim (limt@stat.wisc.edu) (c) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) (c) Date: June 7, 1997 3. Past Usage: Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Machine Learning. Forthcoming. (ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or (http://www.stat.wisc.edu/~limt/mach1317.pdf) 4. Relevant Information: This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics. 5. Number of Instances: 1473 6. Number of Attributes: 10 (including the class attribute) 7. Attribute Information: 1. Wife's age (numerical) 2. Wife's education (categorical) 1=low, 2, 3, 4=high 3. Husband's education (categorical) 1=low, 2, 3, 4=high 4. Number of children ever born (numerical) 5. Wife's religion (binary) 0=Non-Islam, 1=Islam 6. Wife's now working? (binary) 0=Yes, 1=No 7. Husband's occupation (categorical) 1, 2, 3, 4 8. Standard-of-living index (categorical) 1=low, 2, 3, 4=high 9. Media exposure (binary) 0=Good, 1=Not good 10. Contraceptive method used (class attribute) 1=No-use 2=Long-term 3=Short-term 8. Missing Attribute Values: None Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

10 features

Contraceptive_method_used (target)nominal3 unique values
0 missing
Wifes_agenumeric34 unique values
0 missing
Wifes_educationnominal4 unique values
0 missing
Husbands_educationnominal4 unique values
0 missing
Number_of_children_ever_bornnumeric15 unique values
0 missing
Wifes_religionnominal2 unique values
0 missing
Wifes_now_working%3Fnominal2 unique values
0 missing
Husbands_occupationnominal4 unique values
0 missing
Standard-of-living_indexnominal4 unique values
0 missing
Media_exposurenominal2 unique values
0 missing

107 properties

1473
Number of instances (rows) of the dataset.
10
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.
2
Number of numeric attributes.
8
Number of nominal attributes.
1
Average class difference between consecutive instances.
0.66
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
0.49
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
0.23
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
0.66
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
0.49
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
0.23
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
0.66
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
0.49
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
0.23
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
1.54
Entropy of the target attribute values.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.57
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.01
Number of attributes divided by the number of instances.
53.27
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
42.7
Percentage of instances belonging to the most frequent class.
629
Number of instances belonging to the most frequent class.
1.87
Maximum entropy among attributes.
1.53
Maximum kurtosis among attributes of the numeric type.
32.54
Maximum of means among attributes of the numeric type.
0.07
Maximum mutual information between the nominal attributes and the target attribute.
4
The maximum number of distinct values among attributes of the nominal type.
1.1
Maximum skewness among attributes of the numeric type.
8.23
Maximum standard deviation of attributes of the numeric type.
1.22
Average entropy of the attributes.
0.29
Mean kurtosis among attributes of the numeric type.
17.9
Mean of means among attributes of the numeric type.
0.03
Average mutual information between the nominal attributes and the target attribute.
41.26
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3.13
Average number of distinct values among the attributes of the nominal type.
0.68
Mean skewness among attributes of the numeric type.
5.29
Mean standard deviation of attributes of the numeric type.
0.38
Minimal entropy among attributes.
-0.94
Minimum kurtosis among attributes of the numeric type.
3.26
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0.26
Minimum skewness among attributes of the numeric type.
2.36
Minimum standard deviation of attributes of the numeric type.
22.61
Percentage of instances belonging to the least frequent class.
333
Number of instances belonging to the least frequent class.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.51
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3
Number of binary attributes.
30
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
20
Percentage of numeric attributes.
80
Percentage of nominal attributes.
0.61
First quartile of entropy among attributes.
-0.94
First quartile of kurtosis among attributes of the numeric type.
3.26
First quartile of means among attributes of the numeric type.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.26
First quartile of skewness among attributes of the numeric type.
2.36
First quartile of standard deviation of attributes of the numeric type.
1.45
Second quartile (Median) of entropy among attributes.
0.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
17.9
Second quartile (Median) of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
5.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.76
Third quartile of entropy among attributes.
1.53
Third quartile of kurtosis among attributes of the numeric type.
32.54
Third quartile of means among attributes of the numeric type.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
1.1
Third quartile of skewness among attributes of the numeric type.
8.23
Third quartile of standard deviation of attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.99
Standard deviation of the number of distinct values among attributes of the nominal type.
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.55
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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