Summary

(No information yet)

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unknown (from Weka repository)

Dependencies

Tags

arff slurped Weka

Attribute Types

Integer,Floating Point

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# Instances: 158 / # Attributes: 8
HDF5 (21.5 KB) XML CSV ARFF LibSVM Matlab Octave

Original Data Format

arff

Name

'fishcatch'

Version mldata

0

Comment

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Weight treated as the class attribute. Identifier deleted.

As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems. Singapore: Springer-Verlag.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

NAME: fishcatch TYPE: Sample SIZE: 159 observations, 8 variables

DESCRIPTIVE ABSTRACT:

159 fishes of 7 species are caught and measured. Altogether there are 8 variables. All the fishes are caught from the same lake (Laengelmavesi) near Tampere in Finland.

SOURCES: Brofeldt, Pekka: Bidrag till kaennedom on fiskbestondet i vaera sjoear. Laengelmaevesi. T.H.Jaervi: Finlands Fiskeriet Band 4, Meddelanden utgivna av fiskerifoereningen i Finland. Helsingfors 1917

VARIABLE DESCRIPTIONS:

1 Obs Observation number ranges from 1 to 159 2 Species (Numeric) Code Finnish Swedish English Latin
1 Lahna Braxen Bream Abramis brama 2 Siika Iiden Whitewish Leusiscus idus 3 Saerki Moerten Roach Leuciscus rutilus 4 Parkki Bjoerknan ? Abramis bjrkna 5 Norssi Norssen Smelt Osmerus eperlanus 6 Hauki Jaedda Pike Esox lucius 7 Ahven Abborre Perch Perca fluviatilis

3 Weight Weight of the fish (in grams) 4 Length1 Length from the nose to the beginning of the tail (in cm) 5 Length2 Length from the nose to the notch of the tail (in cm) 6 Length3 Length from the nose to the end of the tail (in cm) 7 Height% Maximal height as % of Length3 8 Width% Maximal width as % of Length3 9 Sex 1 = male 0 = female

      ___/////___                  _
     /           \    ___          |
   /\             \_ /  /          H
 <   )            __)  \           |
   \/_\\_________/   \__\          _

 |------- L1 -------|
 |------- L2 ----------|
 |------- L3 ------------|

Values are aligned and delimited by blanks. Missing values are denoted with NA. There is one data line for each case.

SPECIAL NOTES: I have usually calculated Height = Height%Length3/100 Widht = Widht%Length3/100

PEDAGOGICAL NOTES: I have mainly used only Species=7 (Perch) and here is some of the models and test, we have used

  Weight=a+b*(Length3*Height*Width)+epsilon
     Ho: a=0;
     Heteroscedastic case. Question: What is proper weighting, 
     if you use Length3 as a weighting variable.

  Log(Weight)=a+b1*Length3+epsilon

  Weight^(1/3)=a+b1*Length3+epsilon
  (Given by Box-Cox-transformation)
     Ho: a=0;

  Log(Weight)=a+b1*Length3+b2*Height+b3*Width+epsilon
     Ho: b1+b2+b3=3;  
     i.e. dimension of the fish = 3

  Weight^(1/3)=a+b1*Length3+b2*Height+b3*Width+epsilon
  (Given by Box-Cox-transformation)
     Ho: a=0;

  Weight=a*Length3^b1*Height^b2*Width^b3+epsilon
     Nonlinear, heteroscedastic case.
     What is proper weighting?

  Is obs 143

  143  7  840.0 32.5  35.0  37.3  30.8  20.9  0

  an outlier? It had in its stomach 6 roach.

REFERENCES: Brofeldt, Pekka: Bidrag till kaennedom on fiskbestondet i vaara sjoear. Laengelmaevesi. T.H.Jaervi: Finlands Fiskeriet Band 4, Meddelanden utgivna av fiskerifoereningen i Finland. Helsingfors 1917

SUBMITTED BY: Juha Puranen Departement of statistics PL33 (Aleksanterinkatu 7) 000014 University of Helsinki Finland e-mail: jpuranen@noppa.helsinki.fi

Names

Species,Length1,Length2,Length3,Height,Width,Sex,class,

Types

1. nominal:1,2,3,4,5,6,7
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric
7. nominal:1,0
8. numeric

Data (first 10 data points)

Spec...	Leng...	Leng...	Leng...	Height	Width	Sex	class
1.0	23.2	25.0	30.0	38.4	13.4	nan	242.0
1.0	24.0	26.0	31.2	40.0	13.8	nan	290.0
1.0	23.9	26.0	31.1	39.8	15.1	nan	340.0
1.0	26.3	29.0	33.5	38.0	13.3	nan	363.0
1.0	26.5	29.0	34.0	36.6	15.1	nan	430.0
1.0	26.8	29.0	34.7	39.2	14.2	nan	450.0
1.0	26.8	29.0	34.5	41.1	15.3	nan	500.0
1.0	27.6	30.0	35.0	36.2	13.4	nan	390.0
1.0	27.6	30.0	35.1	39.9	13.8	nan	450.0
1.0	28.5	30.0	36.2	39.3	13.7	nan	500.0
...	...	...	...	...	...	...	...

Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

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Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/

Measurement Details

Usage Scenario

revision 1

by mldata on 2011-09-14 15:25

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