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