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wine-quality-red

wine-quality-red

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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wine-quality-red-pmlb

12 features

class (target)nominal6 unique values
0 missing
fixed_aciditynumeric96 unique values
0 missing
volatile_aciditynumeric143 unique values
0 missing
citric_acidnumeric80 unique values
0 missing
residual_sugarnumeric91 unique values
0 missing
chloridesnumeric153 unique values
0 missing
free_sulfur_dioxidenumeric60 unique values
0 missing
total_sulfur_dioxidenumeric144 unique values
0 missing
densitynumeric436 unique values
0 missing
pHnumeric89 unique values
0 missing
sulphatesnumeric96 unique values
0 missing
alcoholnumeric65 unique values
0 missing

62 properties

1599
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
6
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.
10
Number of instances belonging to the least frequent class.
0.53
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.
8.31
Mean kurtosis among attributes of the numeric type.
0.32
First quartile of skewness among attributes of the numeric type.
8.13
Mean of means among attributes of the numeric type.
0.15
First quartile of standard deviation of attributes of the numeric type.
0.45
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
1.71
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.
1.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
6
Average number of distinct values among the attributes of the nominal type.
2.54
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.
1.68
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
42.59
Percentage of instances belonging to the most frequent class.
4.39
Mean standard deviation of attributes of the numeric type.
0.98
Second quartile (Median) of skewness among attributes of the numeric type.
681
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.79
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
41.72
Maximum kurtosis among attributes of the numeric type.
0.09
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
11.72
Third quartile of kurtosis among attributes of the numeric type.
46.47
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.
91.67
Percentage of numeric attributes.
10.42
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
0.07
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.
6
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
2.43
Third quartile of skewness among attributes of the numeric type.
5.68
Maximum skewness among attributes of the numeric type.
0.63
Percentage of instances belonging to the least frequent class.
0.81
First quartile of kurtosis among attributes of the numeric type.
1.74
Third quartile of standard deviation of attributes of the numeric type.
32.9
Maximum standard deviation of attributes of the numeric type.

20 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: alcohol
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: 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
0 runs - estimation_procedure: 50 times Clustering
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