Waveform Database Generator (Version 2)
Donated on 11/9/1988
CART book's waveform domains
Dataset Characteristics
Multivariate, Data-Generator
Subject Area
Physics and Chemistry
Associated Tasks
Classification
Feature Type
Real
# Instances
5000
# Features
-
Dataset Information
Additional Information
Notes: -- 3 classes of waves -- 40 attributes, all of which include noise -- The latter 19 attributes are all noise attributes with mean 0 and variance 1 -- See the book for details (49-55, 169) -- waveform-+noise.data.Z contains 5000 instances
Has Missing Values?
No
Variables Table
Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
---|---|---|---|---|---|---|
no | ||||||
no | ||||||
no | ||||||
no | ||||||
no | ||||||
no | ||||||
no | ||||||
no | ||||||
no | ||||||
no |
0 to 10 of 40
Additional Variable Information
-- Each class is generated from a combination of 2 of 3 "base" waves -- Each instance is generated f added noise (mean 0, variance 1) in each attribute -- See the book for details (49-55, 169)
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset waveform_database_generator_version_2 = fetch_ucirepo(id=108) # data (as pandas dataframes) X = waveform_database_generator_version_2.data.features y = waveform_database_generator_version_2.data.targets # metadata print(waveform_database_generator_version_2.metadata) # variable information print(waveform_database_generator_version_2.variables)
Breiman,L. and Stone,C.J.. (1988). Waveform Database Generator (Version 2). UCI Machine Learning Repository. https://doi.org/10.24432/C56014.
@misc{misc_waveform_database_generator_(version_2)_108, author = {Breiman,L. and Stone,C.J.}, title = {{Waveform Database Generator (Version 2)}}, year = {1988}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C56014} }
Creators
L. Breiman
C.J. Stone
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.