Data
prnn_viruses

prnn_viruses

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
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Author: B.D. Ripley Source: StatLib - Date unknown Please cite: Dataset from `Pattern Recognition and Neural Networks' by B.D. Ripley. Cambridge University Press (1996) ISBN 0-521-46086-7 The background to the datasets is described in section 1.4; this file relates the computer-readable files to that description. viruses This is a dataset on 61 viruses with rod-shaped particles affecting various crops (tobacco, tomato, cucumber and others) described by {Fauquet et al. (1988) and analysed by Eslava-G\'omez (1989). There are 18 measurements on each virus, the number of amino acid residues per molecule of coat protein. The whole dataset is in order Hordeviruses (3), Tobraviruses (6), Tobamoviruses (39) and `furoviruses' (13). These were added as the last (target) attribute

19 features

virus_type (target)nominal4 unique values
0 missing
col_1numeric19 unique values
0 missing
col_2numeric16 unique values
0 missing
col_3numeric13 unique values
0 missing
col_4numeric15 unique values
0 missing
col_5numeric12 unique values
0 missing
col_6numeric17 unique values
0 missing
col_7numeric13 unique values
0 missing
col_8nominal4 unique values
0 missing
col_9numeric13 unique values
0 missing
col_10nominal6 unique values
0 missing
col_11nominal9 unique values
0 missing
col_12nominal10 unique values
0 missing
col_13nominal8 unique values
0 missing
col_14nominal10 unique values
0 missing
col_15nominal7 unique values
0 missing
col_16numeric13 unique values
0 missing
col_17numeric12 unique values
0 missing
col_18nominal6 unique values
0 missing

107 properties

61
Number of instances (rows) of the dataset.
19
Number of attributes (columns) of the dataset.
4
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.
10
Number of numeric attributes.
9
Number of nominal attributes.
63.93
Percentage of instances belonging to the most frequent class.
4.25
Mean standard deviation of attributes of the numeric type.
2.25
Second quartile (Median) of entropy among attributes.
0.39
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.6
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
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
39
Number of instances belonging to the most frequent class.
1.03
Minimal entropy among attributes.
-0.36
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.43
Entropy of the target attribute values.
2.96
Maximum entropy among attributes.
-1.16
Minimum kurtosis among attributes of the numeric type.
13.07
Second quartile (Median) of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.96
Maximum kurtosis among attributes of the numeric type.
4.93
Minimum of means among attributes of the numeric type.
0.38
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.39
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
20.31
Maximum of means among attributes of the numeric type.
0.2
Minimal mutual information between the nominal attributes and the target attribute.
0.48
Second quartile (Median) of skewness among attributes of the numeric type.
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.95
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
4.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.31
Number of attributes divided by the number of instances.
10
The maximum number of distinct values among attributes of the nominal type.
-0.3
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2.79
Third quartile of entropy among attributes.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
2.7
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.57
Maximum skewness among attributes of the numeric type.
2.47
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1.3
Third quartile of kurtosis among attributes of the numeric type.
0.95
Average class difference between consecutive instances.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
8.12
Maximum standard deviation of attributes of the numeric type.
4.92
Percentage of instances belonging to the least frequent class.
52.63
Percentage of numeric attributes.
17.32
Third quartile of means among attributes of the numeric type.
0.83
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.26
Average entropy of the attributes.
3
Number of instances belonging to the least frequent class.
47.37
Percentage of nominal attributes.
0.86
Third quartile of mutual information between the nominal attributes and the target attribute.
0.23
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
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.09
Mean kurtosis among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.98
First quartile of entropy among attributes.
1.22
Third quartile of skewness among attributes of the numeric type.
0.6
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
13.24
Mean of means among attributes of the numeric type.
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.8
First quartile of kurtosis among attributes of the numeric type.
4.46
Third quartile of standard deviation of attributes of the numeric type.
0.83
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.53
Average mutual information between the nominal attributes and the target attribute.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
9.83
First quartile of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.23
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
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.25
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.
0.33
First quartile of mutual information between the nominal attributes and the target attribute.
0.39
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.6
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
7.11
Average number of distinct values among the attributes of the nominal type.
0.11
First quartile of skewness among attributes of the numeric type.
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.83
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
2.32
Standard deviation of the number of distinct values among attributes of the nominal type.
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.64
Mean skewness among attributes of the numeric type.
3.57
First quartile of standard deviation of attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.23
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
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

6 tasks

391 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: virus_type
196 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: virus_type
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: virus_type
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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