Data
wine

wine

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
1 likes downloaded by 16 people , 18 total downloads 0 issues 0 downvotes
  • study_1 study_123 study_41 study_50 study_52 study_7 study_88 wine
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Please cite: 1. Title of Database: Wine recognition data Updated Sept 21, 1998 by C.Blake : Added attribute information 2. Sources: (a) Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy. (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au (c) July 1991 3. Past Usage: (1) S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Technometrics). The data was used with many others for comparing various classifiers. The classes are separable, though only RDA has achieved 100% correct classification. (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) (All results using the leave-one-out technique) In a classification context, this is a well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging. (2) S. Aeberhard, D. Coomans and O. de Vel, "THE CLASSIFICATION PERFORMANCE OF RDA" Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Journal of Chemometrics). Here, the data was used to illustrate the superior performance of the use of a new appreciation function with RDA. 4. Relevant Information: -- These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. -- I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. -- The attributes are (dontated by Riccardo Leardi, riclea@anchem.unige.it ) 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity of ash 5) Magnesium 6) Total phenols 7) Flavanoids 8) Nonflavanoid phenols 9) Proanthocyanins 10)Color intensity 11)Hue 12)OD280/OD315 of diluted wines 13)Proline 5. Number of Instances class 1 59 class 2 71 class 3 48 6. Number of Attributes 13 7. For Each Attribute: All attributes are continuous No statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant) NOTE: 1st attribute is class identifier (1-3) 8. Missing Attribute Values: None 9. Class Distribution: number of instances per class class 1 59 class 2 71 class 3 48 Information about the dataset CLASSTYPE: nominal CLASSINDEX: first

14 features

class (target)nominal3 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%2FOD315_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.
3
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.
-0.84
First quartile of kurtosis among attributes of the numeric type.
2.83
Third quartile of standard deviation of attributes of the numeric type.
0.93
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.09
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
1.81
First quartile of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.09
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
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.86
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
0.87
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.
0
Number of binary attributes.
-0.02
First quartile of skewness among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.93
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.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
0.42
First quartile of standard deviation of attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.09
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
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.35
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.86
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
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
39.89
Percentage of instances belonging to the most frequent class.
26.18
Mean standard deviation of attributes of the numeric type.
-0.25
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.57
Entropy of the target attribute values.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
71
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.37
Second quartile (Median) of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.8
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.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.38
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.
0.21
Second quartile (Median) of skewness among attributes of the numeric type.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.41
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.
0.81
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.94
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.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
Third quartile of entropy among attributes.
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.
3
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.
0.52
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
0.87
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.
16.25
Third quartile of means among attributes of the numeric type.
0.93
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
314.91
Maximum standard deviation of attributes of the numeric type.
26.97
Percentage of instances belonging to the least frequent class.
92.86
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.09
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
0.08
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
48
Number of instances belonging to the least frequent class.
7.14
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.82
Third quartile of skewness among attributes of the numeric type.
0.86
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
0.87
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.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

16 tasks

534 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
294 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
183 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
140 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - 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: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task