Data
pol

pol

active ARFF Publicly available Visibility: public Uploaded 06-01-2023 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 link: https://openml.org/d/201 Original description: Author: Source: Unknown - Please cite: This is a commercial application described in Weiss & Indurkhya (1995). The data describes a telecommunication problem. No further information is available. Characteristics: (10000+5000) cases, 49 continuous attributes Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html Original Source: The data in the original format can be obtained from http://www.cs.su.oz.au/~nitin

27 features

foo (target)numeric11 unique values
0 missing
f5numeric184 unique values
0 missing
f6numeric118 unique values
0 missing
f7numeric114 unique values
0 missing
f8numeric106 unique values
0 missing
f9numeric80 unique values
0 missing
f13numeric97 unique values
0 missing
f14numeric117 unique values
0 missing
f15numeric121 unique values
0 missing
f16numeric120 unique values
0 missing
f17numeric120 unique values
0 missing
f18numeric123 unique values
0 missing
f19numeric102 unique values
0 missing
f20numeric86 unique values
0 missing
f21numeric85 unique values
0 missing
f22numeric88 unique values
0 missing
f23numeric79 unique values
0 missing
f24numeric63 unique values
0 missing
f25numeric68 unique values
0 missing
f26numeric68 unique values
0 missing
f27numeric65 unique values
0 missing
f28numeric64 unique values
0 missing
f29numeric62 unique values
0 missing
f30numeric44 unique values
0 missing
f31numeric43 unique values
0 missing
f32numeric42 unique values
0 missing
f33numeric38 unique values
0 missing

19 properties

15000
Number of instances (rows) of the dataset.
27
Number of attributes (columns) of the dataset.
0
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.
27
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-39.42
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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