Data
analcatdata_supreme

analcatdata_supreme

active ARFF Publicly available Visibility: public Uploaded 18-06-2022 by Leo Grin
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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 categorical and numerical features" benchmark. Original description: Author: Source: Unknown - Date unknown Please cite: analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two versions of each of 84 data sets, plus this README file. Each data set is given in comma-delimited ASCII (.csv) form, and Microsoft Excel (.xls) form. NOTICE: These data sets may be used freely for scientific, educational and/or noncommercial purposes, provided suitable acknowledgment is given (by citing the above-named reference). Further details concerning the book, including information on statistical software (including sample S-PLUS/R and SAS code), are available at the web site http://www.stern.nyu.edu/~jsimonof/AnalCatData Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific Note: Quotes, Single-Quotes and Backslashes were removed, Blanks replaced with Underscores

8 features

Log_exposure (target)numeric10 unique values
0 missing
Actions_takennumeric10 unique values
0 missing
Liberalnominal2 unique values
0 missing
Unconstitutionalnominal2 unique values
0 missing
Precedent_alterationnominal2 unique values
0 missing
Unanimousnominal2 unique values
0 missing
Year_of_decisionnumeric36 unique values
0 missing
Lower_court_disagreementnominal2 unique values
0 missing

19 properties

4052
Number of instances (rows) of the dataset.
8
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.
3
Number of numeric attributes.
5
Number of nominal attributes.
62.5
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.98
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
37.5
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
62.5
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.
5
Number of binary attributes.

0 tasks

Define a new task