Data
cmc

cmc

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • study_14 study_1 study_15 study_17 study_29 study_32 study_48 study_69 study_90 study_92 study_94 study_96 study_98 study_127 study_195 study_256 study_15 study_17 study_29 study_32 study_48 study_69 study_90 study_92 study_94 study_96 study_98 study_127 study_2 study_15 study_17 study_29 study_32 study_48 study_69 study_90 study_92 study_94 study_96 study_98 study_127 study_15 study_17 study_29 study_32 study_48 study_69 study_90 study_92 study_94 study_96 study_98 study_127 study_310 study_15 study_17 study_29 study_32 study_48 study_69 study_90 study_92 study_94 study_96 study_98 study_127 study_15 study_17 study_29 study_32 study_48 study_69 study_90 study_92 study_94 study_96 study_98 study_127 study_346 study_129 study_195
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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
Show all 10 features

107 properties

NumberOfInstances
1473
Number of instances (rows) of the dataset.
NumberOfFeatures
10
Number of attributes (columns) of the dataset.
NumberOfClasses
3
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
2
Number of numeric attributes.
NumberOfSymbolicFeatures
8
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
1.54
Entropy of the target attribute values.
DecisionStumpAUC
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.57
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
53.27
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.5
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
42.7
Percentage of instances belonging to the most frequent class.
MajorityClassSize
629
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1.87
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.53
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
32.54
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.07
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
4
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.1
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
8.23
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.22
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.29
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
17.9
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.03
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
41.26
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
3.13
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.68
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
5.29
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.38
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.94
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
3.26
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.26
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
2.36
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
22.61
Percentage of instances belonging to the least frequent class.
MinorityClassSize
333
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.51
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
3
Number of binary attributes.
PercentageOfBinaryFeatures
30
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
20
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
80
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.61
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.94
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
3.26
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.26
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
2.36
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.45
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
17.9
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
5.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.76
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.53
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
32.54
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.1
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
8.23
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.52
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0.99
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.55
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

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