Data
fishcatch

fishcatch

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 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

8 features

class (target)numeric101 unique values
0 missing
Speciesnominal7 unique values
0 missing
Length1numeric116 unique values
0 missing
Length2numeric93 unique values
0 missing
Length3numeric124 unique values
0 missing
Heightnumeric108 unique values
0 missing
Widthnumeric66 unique values
0 missing
Sexnominal2 unique values
87 missing

19 properties

158
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
87
Number of missing values in the dataset.
87
Number of instances with at least one value missing.
6
Number of numeric attributes.
2
Number of nominal attributes.
12.5
Percentage of binary attributes.
55.06
Percentage of instances having missing values.
-78.25
Average class difference between consecutive instances.
6.88
Percentage of missing values.
0.05
Number of attributes divided by the number of instances.
75
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
25
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

18 tasks

10 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
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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