Data
WineQualityWhite-train

WineQualityWhite-train

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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 Data Set Information: The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult: [Web Link] 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. 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) #autoxgboost #autoweka

12 features

fixedacidnumeric67 unique values
0 missing
volacidnumeric115 unique values
0 missing
citricacidnumeric84 unique values
0 missing
residualsugarnumeric284 unique values
0 missing
chloridesnumeric143 unique values
0 missing
freesulfurnumeric119 unique values
0 missing
totalsulfurnumeric240 unique values
0 missing
densitynumeric794 unique values
0 missing
pHnumeric99 unique values
0 missing
sulphatesnumeric73 unique values
0 missing
alcoholnumeric92 unique values
0 missing
classnominal7 unique values
0 missing

62 properties

3429
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
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.
0.48
First quartile of skewness among attributes of the numeric type.
18.42
Mean of means among attributes of the numeric type.
0.1
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
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.
4.32
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
7
Average number of distinct values among the attributes of the nominal type.
3.19
Second quartile (Median) of means 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.
1.38
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
6.16
Mean standard deviation of attributes of the numeric type.
1.18
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.71
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
40.9
Maximum kurtosis among attributes of the numeric type.
0.05
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
14.02
Third quartile of kurtosis among attributes of the numeric type.
138.15
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
91.67
Percentage of numeric attributes.
10.51
Third quartile of means 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.
8.33
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
0.39
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.5
Third quartile of skewness among attributes of the numeric type.
5.27
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.91
First quartile of kurtosis among attributes of the numeric type.
5.1
Third quartile of standard deviation of attributes of the numeric type.
42.7
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
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.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
8.2
Mean kurtosis among attributes of the numeric type.

16 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task