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 dataset is not the original UCI dataset. It has some discretized features. See version 2 for the original. 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
agenominal5 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
capitalgainnominal5 unique values
0 missing
capitallossnominal5 unique values
0 missing
hoursperweeknominal5 unique values
0 missing
native-countrynominal41 unique values
857 missing

62 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.
2
Number of numeric attributes.
13
Number of nominal attributes.
76.07
Percentage of instances belonging to the most frequent class.
52803.3
Mean standard deviation of attributes of the numeric type.
0.07
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
37155
Number of instances belonging to the most frequent class.
0.36
Minimal entropy among attributes.
0.56
Second quartile (Median) of skewness among attributes of the numeric type.
3.44
Maximum entropy among attributes.
0.63
Minimum kurtosis among attributes of the numeric type.
13.33
Percentage of binary attributes.
52803.3
Second quartile (Median) of standard deviation of attributes of the numeric type.
6.06
Maximum kurtosis among attributes of the numeric type.
10.08
Minimum of means among attributes of the numeric type.
7.41
Percentage of instances having missing values.
2.25
Third quartile of entropy among attributes.
189664.13
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
0.88
Percentage of missing values.
6.06
Third quartile of kurtosis among attributes of the numeric type.
0.17
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
13.33
Percentage of numeric attributes.
189664.13
Third quartile of means among attributes of the numeric type.
41
The maximum number of distinct values among attributes of the nominal type.
-0.32
Minimum skewness among attributes of the numeric type.
86.67
Percentage of nominal attributes.
0.09
Third quartile of mutual information between the nominal attributes and the target attribute.
1.44
Maximum skewness among attributes of the numeric type.
2.57
Minimum standard deviation of attributes of the numeric type.
0.8
First quartile of entropy among attributes.
1.44
Third quartile of skewness among attributes of the numeric type.
105604.03
Maximum standard deviation of attributes of the numeric type.
23.93
Percentage of instances belonging to the least frequent class.
0.63
First quartile of kurtosis among attributes of the numeric type.
105604.03
Third quartile of standard deviation of attributes of the numeric type.
1.6
Average entropy of the attributes.
11687
Number of instances belonging to the least frequent class.
10.08
First quartile of means among attributes of the numeric type.
10.36
Standard deviation of the number of distinct values among attributes of the nominal type.
3.34
Mean kurtosis among attributes of the numeric type.
2
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
94837.11
Mean of means among attributes of the numeric type.
-0.32
First quartile of skewness among attributes of the numeric type.
0.63
Average class difference between consecutive instances.
0.07
Average mutual information between the nominal attributes and the target attribute.
2.57
First quartile of standard deviation of attributes of the numeric type.
0.79
Entropy of the target attribute values.
22.84
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.56
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
9.31
Average number of distinct values among the attributes of the nominal type.
3.34
Second quartile (Median) of kurtosis among attributes of the numeric type.
11.81
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.56
Mean skewness among attributes of the numeric type.
94837.11
Second quartile (Median) of means among attributes of the numeric type.

16 tasks

992 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
656 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class - cost matrix: [[0,0.5],[.5,0]]
222 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
221 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
276 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
304 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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