Data
adult

adult

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Author: Ronny Kohavi and Barry Becker Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Adult) - 1996-05-01 Please cite: Ron Kohavi, "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996 Note: this is the original version from the UCI repository, with training and test sets merged. Prediction task is to determine whether a person makes over 50K a year. Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)) Ronny Kohavi and Barry Becker. Data Mining and Visualization, Silicon Graphics. e-mail: ronnyk '@' live.com for questions.

15 features

class (target)nominal2 unique values
0 missing
agenumeric74 unique values
0 missing
workclassnominal8 unique values
2799 missing
fnlwgtnumeric28523 unique values
0 missing
educationnominal16 unique values
0 missing
education-numnumeric16 unique values
0 missing
marital-statusnominal7 unique values
0 missing
occupationnominal14 unique values
2809 missing
relationshipnominal6 unique values
0 missing
racenominal5 unique values
0 missing
sexnominal2 unique values
0 missing
capital-gainnumeric123 unique values
0 missing
capital-lossnumeric99 unique values
0 missing
hours-per-weeknumeric96 unique values
0 missing
native-countrynominal41 unique values
857 missing

107 properties

48842
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
6465
Number of missing values in the dataset.
3620
Number of instances with at least one value missing.
6
Number of numeric attributes.
9
Number of nominal attributes.
0.63
Average class difference between consecutive instances.
0.88
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
0.14
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
0.56
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
0.88
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
0.14
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
0.56
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
0.88
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
0.14
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
0.56
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
0.79
Entropy of the target attribute values.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
11.07
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
76.07
Percentage of instances belonging to the most frequent class.
37155
Number of instances belonging to the most frequent class.
3.44
Maximum entropy among attributes.
152.69
Maximum kurtosis among attributes of the numeric type.
189664.13
Maximum of means among attributes of the numeric type.
0.17
Maximum mutual information between the nominal attributes and the target attribute.
41
The maximum number of distinct values among attributes of the nominal type.
11.89
Maximum skewness among attributes of the numeric type.
105604.03
Maximum standard deviation of attributes of the numeric type.
1.78
Average entropy of the attributes.
30.36
Mean kurtosis among attributes of the numeric type.
31819.97
Mean of means among attributes of the numeric type.
0.07
Average mutual information between the nominal attributes and the target attribute.
23.83
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
11.22
Average number of distinct values among the attributes of the nominal type.
3.06
Mean skewness among attributes of the numeric type.
18914.62
Mean standard deviation of attributes of the numeric type.
0.8
Minimal entropy among attributes.
-0.18
Minimum kurtosis among attributes of the numeric type.
10.08
Minimum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
-0.32
Minimum skewness among attributes of the numeric type.
2.57
Minimum standard deviation of attributes of the numeric type.
23.93
Percentage of instances belonging to the least frequent class.
11687
Number of instances belonging to the least frequent class.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2
Number of binary attributes.
13.33
Percentage of binary attributes.
7.41
Percentage of instances having missing values.
0.88
Percentage of missing values.
40
Percentage of numeric attributes.
60
Percentage of nominal attributes.
0.83
First quartile of entropy among attributes.
0.42
First quartile of kurtosis among attributes of the numeric type.
31.5
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.
0.1
First quartile of skewness among attributes of the numeric type.
9.94
First quartile of standard deviation of attributes of the numeric type.
1.6
Second quartile (Median) of entropy among attributes.
4.5
Second quartile (Median) of kurtosis among attributes of the numeric type.
63.96
Second quartile (Median) of means among attributes of the numeric type.
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1
Second quartile (Median) of skewness among attributes of the numeric type.
208.36
Second quartile (Median) of standard deviation of attributes of the numeric type.
2.74
Third quartile of entropy among attributes.
53.18
Third quartile of kurtosis among attributes of the numeric type.
48225.33
Third quartile of means among attributes of the numeric type.
0.14
Third quartile of mutual information between the nominal attributes and the target attribute.
6.4
Third quartile of skewness among attributes of the numeric type.
31990.02
Third quartile of standard deviation of attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
12.15
Standard deviation of the number of distinct values among attributes of the nominal type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.21
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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