Data
adult

adult

active ARFF Publicly available Visibility: public Uploaded 09-06-2015 by Joaquin Vanschoren
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  • OpenML-CC18 OpenML100 study_123 study_135 study_14 study_144 study_34 study_99
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Author: Ronny Kohavi and Barry Becker Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Adult) - 1996 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 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)) This is the original version from the UCI repository, with training and test sets merged. ### Variable description Variables are all self-explanatory except __fnlwgt__. This is a proxy for the demographic background of the people: "People with similar demographic characteristics should have similar weights". This similarity-statement is not transferable across the 51 different states. Description from the donor of the database: The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex. We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used. The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population. People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state. ### Relevant papers 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

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.
6
Number of numeric attributes.
9
Number of nominal attributes.
13.33
Percentage of binary attributes.
208.36
Second quartile (Median) of standard deviation of attributes of the numeric type.
3.44
Maximum entropy among attributes.
-0.18
Minimum kurtosis among attributes of the numeric type.
7.41
Percentage of instances having missing values.
2.74
Third quartile of entropy among attributes.
152.69
Maximum kurtosis among attributes of the numeric type.
10.08
Minimum of means among attributes of the numeric type.
0.88
Percentage of missing values.
53.18
Third quartile of kurtosis among attributes of the numeric type.
189664.13
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
40
Percentage of numeric attributes.
48225.33
Third quartile of means 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.
60
Percentage of nominal attributes.
0.14
Third quartile of mutual information between the nominal attributes and the target attribute.
41
The maximum number of distinct values among attributes of the nominal type.
-0.32
Minimum skewness among attributes of the numeric type.
0.83
First quartile of entropy among attributes.
6.4
Third quartile of skewness among attributes of the numeric type.
11.89
Maximum skewness among attributes of the numeric type.
2.57
Minimum standard deviation of attributes of the numeric type.
0.42
First quartile of kurtosis among attributes of the numeric type.
31990.02
Third quartile of standard deviation of 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.
31.5
First quartile of means among attributes of the numeric type.
12.15
Standard deviation of the number of distinct values among attributes of the nominal type.
1.78
Average entropy of the attributes.
11687
Number of instances belonging to the least frequent class.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
30.36
Mean kurtosis among attributes of the numeric type.
2
Number of binary attributes.
0.1
First quartile of skewness among attributes of the numeric type.
31819.97
Mean of means among attributes of the numeric type.
9.94
First quartile of standard deviation of 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.
1.6
Second quartile (Median) of entropy among attributes.
0.79
Entropy of the target attribute values.
23.83
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4.5
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
11.22
Average number of distinct values among the attributes of the nominal type.
63.96
Second quartile (Median) of means among attributes of the numeric type.
11.07
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.06
Mean skewness among attributes of the numeric type.
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
76.07
Percentage of instances belonging to the most frequent class.
18914.62
Mean standard deviation of attributes of the numeric type.
1
Second quartile (Median) of skewness among attributes of the numeric type.
37155
Number of instances belonging to the most frequent class.
0.8
Minimal entropy among attributes.

21 tasks

8279 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 4-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
45 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 - target_feature: class
1297 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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