wine: Wine Data Set

In coatless/ucidata: Collection of Datasets from the UC Irvine Machine Learning Repository

Wine Data Set

Description

This data set is the combination of two datasets that were created, using red and white wine samples. The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model these datasets under a regression approach. The support vector machine model achieved the best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity analysis procedure).

Usage

wine

Format

A data frame with 6497 observations (1599 Red and 4898 White) on the following 12 variables.

• fixed acidity

• volatile acidity

• citric acid

• residual sugar

• chlorides

• free sulfur dioxide

• total sulfur dioxide

• density

• pH

• sulphates

• alcohol

• quality

  • Score between 0 and 10 based on sensor reading

• color

  • "White" or "Red"

Source

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. ISSN: 0167-9236.

References

https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality.names https://archive.ics.uci.edu/ml/datasets/Wine+Quality

coatless/ucidata documentation built on July 29, 2024, 8:12 a.m.