Data
wine

wine

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

14 features

binaryClass (target)nominal2 unique values
0 missing
Alcoholnumeric126 unique values
0 missing
Malic_acidnumeric133 unique values
0 missing
Ashnumeric79 unique values
0 missing
Alcalinity_of_ashnumeric63 unique values
0 missing
Magnesiumnumeric53 unique values
0 missing
Total_phenolsnumeric97 unique values
0 missing
Flavanoidsnumeric132 unique values
0 missing
Nonflavanoid_phenolsnumeric39 unique values
0 missing
Proanthocyaninsnumeric101 unique values
0 missing
Color_intensitynumeric132 unique values
0 missing
Huenumeric78 unique values
0 missing
OD280/OD315_of_diluted_winesnumeric122 unique values
0 missing
Prolinenumeric121 unique values
0 missing

107 properties

178
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
2
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.
13
Number of numeric attributes.
1
Number of nominal attributes.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
314.91
Maximum standard deviation of attributes of the numeric type.
39.89
Percentage of instances belonging to the least frequent class.
92.86
Percentage of numeric attributes.
16.25
Third quartile of means among attributes of the numeric type.
0.94
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
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
71
Number of instances belonging to the least frequent class.
7.14
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.11
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
0.77
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
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Mean kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.82
Third quartile of skewness among attributes of the numeric type.
0.94
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
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
69.13
Mean of means among attributes of the numeric type.
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.84
First quartile of kurtosis among attributes of the numeric type.
2.83
Third quartile of standard deviation of attributes of the numeric type.
0.11
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
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.81
First quartile of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
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
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.02
First quartile of skewness among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.35
Mean skewness among attributes of the numeric type.
0.42
First quartile of standard deviation of attributes of the numeric type.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.77
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
0.03
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
60.11
Percentage of instances belonging to the most frequent class.
26.18
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Entropy of the target attribute values.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
107
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.25
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.09
Minimum kurtosis among attributes of the numeric type.
2.37
Second quartile (Median) of means among attributes of the numeric type.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
2.1
Maximum kurtosis among attributes of the numeric type.
0.36
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
746.89
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.21
Second quartile (Median) of skewness among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.08
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
7.14
Percentage of binary attributes.
0.81
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.31
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.1
Maximum skewness among attributes of the numeric type.
0.12
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.52
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.

7 tasks

560 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
220 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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