WINE: WINE data
In regclass: Tools for an Introductory Class in Regression and Modeling
Description
Usage
Format
Details
References
Description
Predicting the quality of wine based on its chemical characteristics
Usage
1
data("WINE")
Format
A data frame with 2700 observations on the following 12 variables.
Qualitya factor with levels high low
fixed.aciditya numeric vector
volatile.aciditya numeric vector
citric.acida numeric vector
residual.sugara numeric vector
chloridesa numeric vector
free.sulfur.dioxidea numeric vector
total.sulfur.dioxidea numeric vector
densitya numeric vector
pHa numeric vector
sulphatesa numeric vector
alcohola numeric vector
Details
This is the famous wine dataset from the UCI data repository https://archive.ics.uci.edu/ml/datasets/Wine+Quality with some modifications. Namely, the quality in the original data was a score between 0 and 10. These has been coded as either high or low. See description on UCI for description of variables.
References
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
regclass documentation built on March 26, 2020, 8:02 p.m.
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Description Usage Format Details References
Description
Predicting the quality of wine based on its chemical characteristics
Usage
1 | data("WINE")
|
Format
A data frame with 2700 observations on the following 12 variables.
Qualitya factor with levels
highlowfixed.aciditya numeric vector
volatile.aciditya numeric vector
citric.acida numeric vector
residual.sugara numeric vector
chloridesa numeric vector
free.sulfur.dioxidea numeric vector
total.sulfur.dioxidea numeric vector
densitya numeric vector
pHa numeric vector
sulphatesa numeric vector
alcohola numeric vector
Details
This is the famous wine dataset from the UCI data repository https://archive.ics.uci.edu/ml/datasets/Wine+Quality with some modifications. Namely, the quality in the original data was a score between 0 and 10. These has been coded as either high or low. See description on UCI for description of variables.
References
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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