DGP2 - The Second Data Generation Program
Generates application domains based on specific parameters, number of features, and proportion of positive to negative examples
Dataset Characteristics
Data-Generator
Subject Area
Other
Associated Tasks
-
Feature Type
Real
# Instances
-
# Features
-
Dataset Information
Additional Information
DGP/2 is an improvement of DGP. It allows for additional parameters and automates the setting of the standard deviation parameter, which is not easily done by the user. In particular, DGP/2 allows for variation in the number of instances, the number of features, the range of feature values, the number of peaks, the percent of positive instances desired and a radius around the peaks that these instances will fall within (this controls instance density, and determines the standard deviation value for the normal distribution function).
Has Missing Values?
No
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset dgp2_the_second_data_generation_program = fetch_ucirepo(id=35) # data (as pandas dataframes) X = dgp2_the_second_data_generation_program.data.features y = dgp2_the_second_data_generation_program.data.targets # metadata print(dgp2_the_second_data_generation_program.metadata) # variable information print(dgp2_the_second_data_generation_program.variables)
Benedict,Powell. DGP2 - The Second Data Generation Program. UCI Machine Learning Repository. https://doi.org/10.24432/C5P02V.
@misc{misc_dgp2_-_the_second_data_generation_program_35,
author = {Benedict,Powell},
title = {{DGP2 - The Second Data Generation Program}},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5P02V}
}Creators
Powell Benedict
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.