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prnn_viruses

prnn_viruses

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Felicia West
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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_10nominal6 unique values
0 missing
col_18nominal6 unique values
0 missing
col_17numeric12 unique values
0 missing
col_16numeric13 unique values
0 missing
col_15nominal7 unique values
0 missing
col_14nominal10 unique values
0 missing
col_13nominal8 unique values
0 missing
col_12nominal10 unique values
0 missing
col_11nominal9 unique values
0 missing
col_1numeric19 unique values
0 missing
col_9numeric13 unique values
0 missing
col_8nominal4 unique values
0 missing
col_7numeric13 unique values
0 missing
col_6numeric17 unique values
0 missing
col_5numeric12 unique values
0 missing
col_4numeric15 unique values
0 missing
col_3numeric13 unique values
0 missing
col_2numeric16 unique values
0 missing
Show all 19 features

107 properties

NumberOfInstances
61
Number of instances (rows) of the dataset.
NumberOfFeatures
19
Number of attributes (columns) of the dataset.
NumberOfClasses
4
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
10
Number of numeric attributes.
NumberOfSymbolicFeatures
9
Number of nominal attributes.
AutoCorrelation
0.95
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
1.43
Entropy of the target attribute values.
DecisionStumpAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.31
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
2.7
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
63.93
Percentage of instances belonging to the most frequent class.
MajorityClassSize
39
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
2.96
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.96
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
20.31
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.95
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.57
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
8.12
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
2.26
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.09
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
13.24
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.53
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
3.25
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
7.11
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.64
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
4.25
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
1.03
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.16
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
4.93
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.2
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
4
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.3
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
2.47
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
4.92
Percentage of instances belonging to the least frequent class.
MinorityClassSize
3
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.91
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
52.63
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
47.37
Percentage of nominal attributes.
Quartile1AttributeEntropy
1.98
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.8
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
9.83
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.33
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.11
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
3.57
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
2.25
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.36
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
13.07
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.38
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.48
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
4.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
2.79
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.3
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
17.32
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.86
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.22
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
4.46
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
2.32
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Classification on prnn_viruses
391 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: virus_type
Supervised Classification on prnn_viruses
196 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: virus_type
Supervised Data Stream Classification on prnn_viruses
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: virus_type
Clustering on prnn_viruses
0 runs
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_viruses
0 runs - estimation_procedure: 50 times Clustering
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