Data
fishcatch

fishcatch

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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

107 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.
0.47
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
398.7
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
12.5
Percentage of binary attributes.
10.39
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.05
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
55.06
Percentage of instances having missing values.
Third quartile of entropy among attributes.
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.
7
The maximum number of distinct values among attributes of the nominal type.
-0.47
Minimum skewness among attributes of the numeric type.
6.88
Percentage of missing values.
0.53
Third quartile of kurtosis among attributes of the numeric type.
-78.25
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.1
Maximum skewness among attributes of the numeric type.
2.28
Minimum standard deviation of attributes of the numeric type.
75
Percentage of numeric attributes.
123.07
Third quartile of means among attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
359.09
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
25
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
0.72
Third quartile of skewness among attributes of the numeric type.
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.17
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.19
First quartile of kurtosis among attributes of the numeric type.
98.5
Third quartile of standard deviation of attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
87.81
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
23.2
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
-0.02
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
3.54
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.5
Average number of distinct values among the attributes of the nominal type.
6.79
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.38
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
67.01
Mean standard deviation of attributes of the numeric type.
0.33
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
28.32
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.99
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.86
Maximum kurtosis among attributes of the numeric type.
14.12
Minimum of means among attributes of the numeric type.

19 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Species
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: 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: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - 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: 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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