Data
prnn_crabs

prnn_crabs

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
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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

19 properties

200
Number of instances (rows) of the dataset.
8
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.
6
Number of numeric attributes.
2
Number of nominal attributes.
25
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.99
Average class difference between consecutive instances.
0
Percentage of missing values.
0.04
Number of attributes divided by the number of instances.
75
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
25
Percentage of nominal attributes.
100
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the least frequent class.
100
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

15 tasks

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