OpenML
white_wine

white_wine

active ARFF CC BY 4.0 Visibility: public Uploaded 22-12-2022 by Sebastian Fischer
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Data Description The data were collected from May/2004 to February/2007 using only protected designation of origin samples that were tested at the official certification entity (CVRVV). The CVRVV is an inter-professional organization with the goal of improving the quality and marketing of vinho verde. The data were recorded by a computerized system (iLab), which automatically manages the process of wine sample testing from producer requests to laboratory and sensory analysis. Each entry denotes a given test (analytical or sensory). The goal is using chemical analysis determine the quality of the wine. This version of the dataset contains only the white wine. Attribute Description 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* 12. *quality* - target feature

12 features

quality (target)numeric7 unique values
0 missing
fixed_aciditynumeric68 unique values
0 missing
volatile_aciditynumeric125 unique values
0 missing
citric_acidnumeric87 unique values
0 missing
residual_sugarnumeric310 unique values
0 missing
chloridesnumeric160 unique values
0 missing
free_sulfur_dioxidenumeric132 unique values
0 missing
total_sulfur_dioxidenumeric251 unique values
0 missing
densitynumeric890 unique values
0 missing
pHnumeric103 unique values
0 missing
sulphatesnumeric79 unique values
0 missing
alcoholnumeric103 unique values
0 missing

19 properties

4898
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
12
Number of numeric attributes.
0
Number of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.2
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: quality
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