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prnn_crabs

prnn_crabs

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Author: Source: Unknown - Date unknown Please cite: Datasets for `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. Cushing's syndrome ------------------ Data from Aitchison & Dunsmore (1975, Tables 11.1-3). Data file Cushings.dat has four columns, Label of the patient Tetrhydrocortisone (mg/24hr) Pregnanetriol (mg/24hr) Type The type of the last six patients (u1 to u6) should be regarded as unknown. (The code `o' indicates `other'). synthetic two-class problem --------------------------- Data from Ripley (1994a). This has two real-valued co-ordinates (xs and ys) and a class (xc) which is 0 or 1. Data file synth.tr has 250 rows of the training set synth.te has 1000 rows of the test set (not used here) 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. Data file viruses.dat has 61 rows of 18 counts virus3.dat has 38 rows corresponding to the distinct Tobamoviruses. The whole dataset is in order Hordeviruses (3), Tobraviruses (6), Tobamoviruses (39) and `furoviruses' (13). Leptograpsus crabs ------------------ Data from Campbell & Mahon (1974) on the morphology of rock crabs of genus Leptograpsus. There are 50 specimens of each sex of each of two colour forms. Data file crabs.dat has rows sp `species', coded B (blue form) or O (orange form) sex coded M or F index within each group of 50 FL frontal lip of carapace (mm) RW rear width of carapace (mm) CL length along the midline of carapace (mm) CW maximum width of carapace (mm) BD body depth (mm) Forensic glass -------------- This example comes from forensic testing of glass collected by B. German on 214 fragments of glass. It is also contained in the UCI machine-learning database collection (Murphy & Aha, 1995). Data file fglass.dat has 214 rows with data for a single glass fragment. RI refractive index Na % weight of sodium oxide(s) Mg % weight of magnesium oxide(s) Al % weight of aluminium oxide(s) Si % weight of silicon oxide(s) K % weight of potassium oxide(s) Ca % weight of calcium oxide(s) Ba % weight of barium oxide(s) Fe % weight of iron oxide(s) type coded 1 to 7 The type codes are: 1 (WinF) window float glass 2 (WinNF) window non-float glass 3 (Veh) vehicle glass 5 (Con) containers 6 (Tabl) tableware 7 (Head) vehicle headlamp glass The ten groups used for the cross-validation experiments (I believe) are listed as row numbers in the file fglass.grp, Diabetes in Pima Indians ------------------------ A population of women who were at least 21 years old, of Pima Indian heritage and living near Phoenix, Arizona, was tested for diabetes according to World Health Organization criteria. The data were collected by the US National Institute of Diabetes and Digestive and Kidney Diseases (Smith et al, 1988). This example is also contained in the UCI machine-learning database collection (Murphy & Aha, 1995). The data files have rows containing npreg number of pregnancies glu plasma glucose concentration in an oral glucose tolerance test bp diastolic blood pressure (mm Hg) skin triceps skin fold thickness (mm) ins serum insulin (micro U/ml) bmi body mass index (weight in kg/(height in m)^2) ped diabetes pedigree function age in years type No / Yes Data file pima.tr has 200 rows of complete training data. pima.te has 332 rows of complete test data. pima.tr2 has the 200 rows of pima.tr plus 100 incomplete rows. Information about the dataset CLASSTYPE: nominal CLASSINDEX: 1

8 features

sp (target)nominal2 unique values
0 missing
sexnominal2 unique values
0 missing
indexnumeric50 unique values
0 missing
FLnumeric104 unique values
0 missing
RWnumeric91 unique values
0 missing
CLnumeric152 unique values
0 missing
CWnumeric145 unique values
0 missing
BDnumeric102 unique values
0 missing
Show all 8 features

107 properties

NumberOfInstances
200
Number of instances (rows) of the dataset.
NumberOfFeatures
8
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
6
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
AutoCorrelation
0.99
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.71
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
0.34
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
0.33
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
0.71
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
0.34
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
0.33
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
0.71
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
0.34
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
0.33
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
Entropy of the target attribute values.
DecisionStumpAUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.04
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
MajorityClassSize
100
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
-0.28
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
36.41
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.03
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
14.47
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.63
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
22.73
Mean of means among attributes of the numeric type.
MeanMutualInformation
0
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
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0.02
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
6.49
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
1
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.2
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
12.74
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.1
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
2.57
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
MinorityClassSize
100
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.41
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
2
Number of binary attributes.
PercentageOfBinaryFeatures
25
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
75
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
25
Percentage of nominal attributes.
Quartile1AttributeEntropy
1
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.76
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
13.71
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.08
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
3.21
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.56
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
20.54
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.01
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
5.31
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.49
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
33.18
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.02
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
9.52
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on prnn_crabs
546 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: sp
Supervised Classification on prnn_crabs
197 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: sp
Supervised Classification on prnn_crabs
0 runs - estimation_procedure: 33% Holdout set - target_feature: sp
Supervised Data Stream Classification on prnn_crabs
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: sp
Clustering on prnn_crabs
0 runs
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
Clustering on prnn_crabs
0 runs - estimation_procedure: 50 times Clustering
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