Data
wine-quality-white

wine-quality-white

active ARFF Publicly available Visibility: public Uploaded 29-07-2016 by Rafael G. Mantovani
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Citation Request: This dataset is public available for research. The details are described in [Cortez et al., 2009]. Please include this citation if you plan to use this database: 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. Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib 1. Title: Wine Quality 2. Sources Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009 3. Past Usage: 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. In the above reference, 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). 4. Relevant Information: 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.). These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are munch more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods. 5. Number of Instances: red wine - 1599; white wine - 4898. 6. Number of Attributes: 11 + output attribute Note: several of the attributes may be correlated, thus it makes sense to apply some sort of feature selection. 7. Attribute information: For more information, read [Cortez et al., 2009]. 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): 12 - quality (score between 0 and 10) 8. Missing Attribute Values: None

12 features

Class (target)nominal7 unique values
0 missing
V1numeric68 unique values
0 missing
V2numeric125 unique values
0 missing
V3numeric87 unique values
0 missing
V4numeric310 unique values
0 missing
V5numeric160 unique values
0 missing
V6numeric132 unique values
0 missing
V7numeric251 unique values
0 missing
V8numeric890 unique values
0 missing
V9numeric103 unique values
0 missing
V10numeric79 unique values
0 missing
V11numeric103 unique values
0 missing

62 properties

4898
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
7
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.
11
Number of numeric attributes.
1
Number of nominal attributes.
Average entropy of the attributes.
5
Number of instances belonging to the least frequent class.
0.33
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
7.07
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
18.43
Mean of means among attributes of the numeric type.
0.49
First quartile of skewness among attributes of the numeric type.
0.41
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.1
First quartile of standard deviation of attributes of the numeric type.
1.86
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
7
Average number of distinct values among the attributes of the nominal type.
3.47
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.3
Mean skewness among attributes of the numeric type.
3.19
Second quartile (Median) of means among attributes of the numeric type.
44.88
Percentage of instances belonging to the most frequent class.
6.11
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
2198
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.98
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.7
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
37.56
Maximum kurtosis among attributes of the numeric type.
0.05
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
138.36
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
9.79
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
7
The minimal number of distinct values among attributes of the nominal type.
91.67
Percentage of numeric attributes.
10.51
Third quartile of means among attributes of the numeric type.
7
The maximum number of distinct values among attributes of the nominal type.
0.39
Minimum skewness among attributes of the numeric type.
8.33
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
5.02
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.41
Third quartile of skewness among attributes of the numeric type.
42.5
Maximum standard deviation of attributes of the numeric type.
0.1
Percentage of instances belonging to the least frequent class.
0.57
First quartile of kurtosis among attributes of the numeric type.
5.07
Third quartile of standard deviation of attributes of the numeric type.

7 tasks

33 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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