wine_quality

  • Description:

Two datasets 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).

The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

Number of Instances: red wine - 1599; white wine - 4898

Input variables (based on physicochemical tests):

  1. fixed acidity
  2. volatile acidity
  3. citric acid
  4. residual sugar
  5. chlorides
  6. free sulfur dioxide
  7. total sulfur dioxide
  8. density
  9. pH
  10. sulphates
  11. alcohol

Output variable (based on sensory data):

  1. quality (score between 0 and 10)
FeaturesDict({
    'features': FeaturesDict({
        'alcohol': float32,
        'chlorides': float32,
        'citric acid': float32,
        'density': float32,
        'fixed acidity': float32,
        'free sulfur dioxide': float32,
        'pH': float32,
        'residual sugar': float32,
        'sulphates': float64,
        'total sulfur dioxide': float32,
        'volatile acidity': float32,
    }),
    'quality': int32,
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
features FeaturesDict
features/alcohol Tensor float32
features/chlorides Tensor float32
features/citric acid Tensor float32
features/density Tensor float32
features/fixed acidity Tensor float32
features/free sulfur dioxide Tensor float32
features/pH Tensor float32
features/residual sugar Tensor float32
features/sulphates Tensor float64
features/total sulfur dioxide Tensor float32
features/volatile acidity Tensor float32
quality Tensor int32 
@ONLINE {cortezpaulo;cerdeiraantonio;almeidafernando;matostelmo;reisjose1999,
    author = "Cortez, Paulo; Cerdeira, Antonio; Almeida,Fernando;  Matos, Telmo;  Reis, Jose",
    title  = "Modeling wine preferences by data mining from physicochemical properties.",
    year   = "2009",
    url    = "https://archive.ics.uci.edu/ml/datasets/wine+quality"
}

wine_quality/white (default config)

  • Config description: White Wine

  • Download size: 258.23 KiB

  • Dataset size: 1.87 MiB

  • Splits:

Split Examples
'train' 4,898

wine_quality/red

  • Config description: Red Wine

  • Download size: 82.23 KiB

  • Dataset size: 626.17 KiB

  • Splits:

Split Examples
'train' 1,599