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wine_quality

wine_quality

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Author: Tobias Kuehn Source: Unknown - 2009 Please cite: 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 - first 1599 instances; white wine - instances 1600 to 6497. 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

quality (target)numeric7 unique values
0 missing
fixed.aciditynumeric106 unique values
0 missing
volatile.aciditynumeric187 unique values
0 missing
citric.acidnumeric89 unique values
0 missing
residual.sugarnumeric316 unique values
0 missing
chloridesnumeric214 unique values
0 missing
free.sulfur.dioxidenumeric135 unique values
0 missing
total.sulfur.dioxidenumeric276 unique values
0 missing
densitynumeric998 unique values
0 missing
pHnumeric108 unique values
0 missing
sulphatesnumeric111 unique values
0 missing
alcoholnumeric111 unique values
0 missing
Show all 12 features

107 properties

NumberOfInstances
6497
Number of instances (rows) of the dataset.
NumberOfFeatures
12
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
12
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
AutoCorrelation
0.22
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
50.9
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
115.74
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
5.4
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
56.52
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
7.37
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
15.06
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.27
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
6.92
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.53
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.06
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.27
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.39
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.41
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.15
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
3.59
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
4.33
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.89
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.52
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
7.58
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
9.67
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.67
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
3.89
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Regression on wine_quality
3 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: quality
Supervised Regression on wine_quality
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: quality
Supervised Regression on wine_quality
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: quality
Supervised Regression on wine_quality
0 runs - estimation_procedure: 33% Holdout set - target_feature: quality
Clustering on wine_quality
0 runs
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
Clustering on wine_quality
0 runs - estimation_procedure: 50 times Clustering
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