Wine quality
Abstract: Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009]).
http://archive.ics.uci.edu/ml/datasets/Wine+Quality
Data
fixed acidity |
volatile acidity |
citric acid |
residual sugar |
chlorides |
free sulfur dioxide |
total sulfur dioxide |
density |
pH |
sulphates |
alcohol |
quality |
7 |
0.27 |
0.36 |
20.7 |
0.045 |
45 |
170 |
1.001 |
3 |
0.45 |
8.8 |
6 |
6.3 |
0.3 |
0.34 |
1.6 |
0.049 |
14 |
132 |
0.994 |
3.3 |
0.49 |
9.5 |
6 |
8.1 |
0.28 |
0.4 |
6.9 |
0.05 |
30 |
97 |
0.9951 |
3.26 |
0.44 |
10.1 |
6 |
Example
This example demonstrate the application of a regressor:
- with 100 epochs, 70 percent training data, 0.9 learning with 0.001 decay
- Regression threshold of 0.2
- 100 hidden neurons
Learning
The learning here is that the regressor decides between correct vs. wrong classified using a threshold. You can change this threshold to bring more tolerance to the system.
Source
Paulo Cortez, University of Minho, Guimarães, Portugal, http://www3.dsi.uminho.pt/pcortez
A. Cerdeira, F. Almeida, T. Matos and J. Reis, Viticulture Commission of the Vinho Verde Region(CVRVV), Porto, Portugal
@2009