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prnn_cushings

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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: last

Type (target) | nominal | 4 unique values 0 missing | |

Label (ignore) | nominal | 27 unique values 0 missing | |

Tetrahydrocortisone | numeric | 25 unique values 0 missing | |

Pregnanetriol | numeric | 19 unique values 0 missing |

0.67

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

6.55

Second quartile (Median) of kurtosis among attributes of the numeric type.

0.35

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

6.35

Second quartile (Median) of means among attributes of the numeric type.

0.75

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.68

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.41

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

Minimal mutual information between the nominal attributes and the target attribute.

2.37

Second quartile (Median) of skewness among attributes of the numeric type.

0.35

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.33

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

Maximum mutual information between the nominal attributes and the target attribute.

4

The minimal number of distinct values among attributes of the nominal type.

6.87

Second quartile (Median) of standard deviation of attributes of the numeric type.

0.79

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

4

The maximum number of distinct values among attributes of the nominal type.

0.26

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.

0.61

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.82

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.82

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

0.79

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Third quartile of mutual information between the nominal attributes and the target attribute.

0.22

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

0.26

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.67

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.8

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

2.86

Third quartile of skewness among attributes of the numeric type.

0.67

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

0.61

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.82

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

10.77

Third quartile of standard deviation of attributes of the numeric type.

0.82

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

0.79

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Average mutual information between the nominal attributes and the target attribute.

0.75

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.22

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

0.26

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.67

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

First quartile of mutual information between the nominal attributes and the target attribute.

0.67

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

0.61

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.82

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

4

Average number of distinct values among the attributes of the nominal type.

1.88

First quartile of skewness among attributes of the numeric type.

0.35

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.82

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.

2.97

First quartile of standard deviation of attributes of the numeric type.

0.75

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.22

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