Data
kdd_ipums_la_97-small

kdd_ipums_la_97-small

active ARFF Publicly available Visibility: public Uploaded 05-07-2022 by Leo Grin
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description: Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

21 features

binaryClass (target)nominal2 unique values
0 missing
valuenumeric11 unique values
0 missing
rentnumeric134 unique values
0 missing
ftotincnumeric395 unique values
0 missing
momlocnumeric11 unique values
0 missing
famsizenumeric15 unique values
0 missing
nchildnumeric10 unique values
0 missing
eldchnumeric62 unique values
0 missing
yngchnumeric60 unique values
0 missing
nsibsnumeric10 unique values
0 missing
agenumeric97 unique values
0 missing
occscorenumeric45 unique values
0 missing
seinumeric80 unique values
0 missing
inctotnumeric284 unique values
0 missing
incwagenumeric216 unique values
0 missing
incbusnumeric105 unique values
0 missing
incfarmnumeric18 unique values
0 missing
incssnumeric38 unique values
0 missing
incwelfrnumeric37 unique values
0 missing
incothernumeric93 unique values
0 missing
povertynumeric479 unique values
0 missing

19 properties

5188
Number of instances (rows) of the dataset.
21
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.
20
Number of numeric attributes.
1
Number of nominal attributes.
4.76
Percentage of binary attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
95.24
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
4.76
Percentage of nominal attributes.
2594
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the least frequent class.
2594
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

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