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adult

adult

active ARFF Publicly available Visibility: public Uploaded 27-01-2023 by Smith
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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))

15 features

class (target)string2 unique values
0 missing
agenumeric74 unique values
0 missing
fnlwgtnumeric28523 unique values
0 missing
education-numnumeric16 unique values
0 missing
capital-gainnumeric123 unique values
0 missing
capital-lossnumeric99 unique values
0 missing
hours-per-weeknumeric96 unique values
0 missing
workclassnominal9 unique values
0 missing
educationnominal16 unique values
0 missing
marital-statusnominal7 unique values
0 missing
occupationnominal15 unique values
0 missing
relationshipnominal6 unique values
0 missing
racenominal5 unique values
0 missing
sexnominal2 unique values
0 missing
native-countrynominal42 unique values
0 missing

19 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).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
6
Number of numeric attributes.
8
Number of nominal attributes.
53.33
Percentage of nominal attributes.
1
Average class difference between consecutive instances.
40
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
6.67
Percentage of binary attributes.
1
Number of binary attributes.
11687
Number of instances belonging to the least frequent class.
23.93
Percentage of instances belonging to the least frequent class.
37155
Number of instances belonging to the most frequent class.
76.07
Percentage of instances belonging to the most frequent class.
0
Number of attributes divided by the number of instances.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
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