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cmc

cmc

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  • OpenML-CC18 OpenML100 study_1 study_123 study_14 study_34 study_37 study_41 study_50 study_7 study_70 study_98 study_99 uci
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Author: [Tjen-Sien Lim](limt@stat.wisc.edu) Source: [As obtained from UCI](https://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice) Please cite: [UCI citation](https://archive.ics.uci.edu/ml/citation_policy.html) 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.
41.26
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.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.13
Average number of distinct values among the attributes of the nominal type.
0.26
First quartile of skewness among attributes of the numeric type.
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.99
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
0.68
Mean skewness among attributes of the numeric type.
2.36
First 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 2
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
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.29
Mean standard deviation of attributes of the numeric type.
1.45
Second quartile (Median) of entropy among attributes.
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.55
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
42.7
Percentage of instances belonging to the most frequent class.
0.38
Minimal entropy among attributes.
0.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.54
Entropy of the target attribute values.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
629
Number of instances belonging to the most frequent class.
-0.94
Minimum kurtosis among attributes of the numeric type.
17.9
Second quartile (Median) of means among attributes of the numeric type.
0.67
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
1.87
Maximum entropy among attributes.
3.26
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.
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.57
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.53
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
32.54
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
30
Percentage of binary attributes.
5.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.6
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.
0.07
Maximum mutual information between the nominal attributes and the target attribute.
0.26
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.76
Third quartile of entropy among attributes.
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
53.27
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
2.36
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1.53
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.1
Maximum skewness among attributes of the numeric type.
22.61
Percentage of instances belonging to the least frequent class.
20
Percentage of numeric attributes.
32.54
Third quartile of means among attributes of the numeric type.
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
0.6
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
8.23
Maximum standard deviation of attributes of the numeric type.
333
Number of instances belonging to the least frequent class.
80
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.22
Average entropy of the attributes.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.61
First quartile of entropy among attributes.
1.1
Third quartile of skewness among attributes of the numeric type.
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.29
Mean kurtosis among attributes of the numeric type.
0.51
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.94
First quartile of kurtosis among attributes of the numeric type.
8.23
Third quartile of standard deviation of attributes of the numeric type.
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
0.6
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
17.9
Mean of means among attributes of the numeric type.
0.03
Average mutual information between the nominal attributes and the target attribute.
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.26
First quartile of means among 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.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

24 tasks

16287 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Contraceptive_method_used
301 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Contraceptive_method_used
299 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Contraceptive_method_used
174 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Contraceptive_method_used
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Contraceptive_method_used
0 runs - estimation_procedure: 33% Holdout set - target_feature: Contraceptive_method_used
303 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Contraceptive_method_used
170 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Contraceptive_method_used
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
1310 runs - target_feature: Contraceptive_method_used
1307 runs - target_feature: Contraceptive_method_used
1303 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
0 runs - target_feature: Contraceptive_method_used
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