Data
grub-damage

grub-damage

active ARFF Publicly available Visibility: public Uploaded 26-08-2014 by Joaquin Vanschoren
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Author: R. J. Townsend Source: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) - Please cite: Grass Grubs and Damage Ranking Data source: R. J. Townsend AgResearch, Lincoln, New Zealand Grass grubs are one of the major insect pests of pasture in Canterbury and can cause severe pasture damage and economic loss. Pastoral damage may occur periodically over wide ranging areas. Grass grub populations are often influenced by biotic factors (diseases) and farming practices (such as irrigation and heavy rolling). The objective of the report was to report on grass grub population and damage levels to provide objective estimates of the annual losses caused by grass grubs. The original machine learning objective was to find a relationship between grass grub numbers, irrigation and damage ranking for the period between 1986 to 1992. Attribute Information: 1. year_zone - Years 0, 1, 2, 6, 7, 8, 9 divided into three zones: f, m, c - enumerated 2. year - year of trial - enumerated 3. strip - strip of paddock sampled - integer 4. pdk - paddock sampled - integer 5. damage_rankRJT - RJ Townsends damage ranking - enumerated 6. damage_rankALL - other researchers damage ranking - enumerated 7. dry_or_irr - indicates if paddock was dry or irrigated (D: dryland, O: irrigated overhead, B: irrigated border dyke) - enumerated 8. zone - position of paddock (F: foothills, M: midplain, C: coastal) - enumerated 9. GG_new - based on grass grubs per metre squared - enumerated

9 features

GG_new (target)nominal4 unique values
0 missing
year_zonenominal21 unique values
0 missing
yearnominal7 unique values
0 missing
stripnumeric9 unique values
0 missing
pdknumeric6 unique values
0 missing
damage_rankRJTnominal6 unique values
0 missing
damage_rankALLnominal6 unique values
0 missing
dry_or_irrnominal3 unique values
0 missing
zonenominal3 unique values
0 missing

107 properties

155
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
4
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.
2
Number of numeric attributes.
7
Number of nominal attributes.
8.58
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
0.16
First quartile of mutual information between the nominal attributes and the target attribute.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-0
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
7.14
Average number of distinct values among the attributes of the nominal type.
0.22
First quartile of skewness among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.54
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
6.31
Standard deviation of the number of distinct values among attributes of the nominal type.
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.38
Mean skewness among attributes of the numeric type.
1.07
First quartile of standard deviation of attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.72
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.09
Mean standard deviation of attributes of the numeric type.
2.44
Second quartile (Median) of entropy among attributes.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-0
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
0.66
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
31.61
Percentage of instances belonging to the most frequent class.
0.94
Minimal entropy among attributes.
-0.79
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.92
Entropy of the target attribute values.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
49
Number of instances belonging to the most frequent class.
-1.28
Minimum kurtosis among attributes of the numeric type.
3.76
Second quartile (Median) of means among attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4.31
Maximum entropy among attributes.
2.18
Minimum of means among attributes of the numeric type.
0.18
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.68
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.29
Maximum kurtosis among attributes of the numeric type.
0.14
Minimal mutual information between the nominal attributes and the target attribute.
0.38
Second quartile (Median) of skewness among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
5.34
Maximum of means among attributes of the numeric type.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
2.09
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.06
Number of attributes divided by the number of instances.
0.59
Maximum mutual information between the nominal attributes and the target attribute.
0.22
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
3.16
Third quartile of entropy among attributes.
0.67
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
7.65
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
21
The maximum number of distinct values among attributes of the nominal type.
1.07
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.29
Third quartile of kurtosis among attributes of the numeric type.
0.35
Average class difference between consecutive instances.
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.54
Maximum skewness among attributes of the numeric type.
12.26
Percentage of instances belonging to the least frequent class.
22.22
Percentage of numeric attributes.
5.34
Third quartile of means among attributes of the numeric type.
0.54
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.11
Maximum standard deviation of attributes of the numeric type.
19
Number of instances belonging to the least frequent class.
77.78
Percentage of nominal attributes.
0.33
Third quartile of mutual information between the nominal attributes and the target attribute.
0.72
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
0.67
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.41
Average entropy of the attributes.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.39
First quartile of entropy among attributes.
0.54
Third quartile of skewness among attributes of the numeric type.
-0
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.79
Mean kurtosis among attributes of the numeric type.
0.55
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.28
First quartile of kurtosis among attributes of the numeric type.
3.11
Third quartile of standard deviation of attributes of the numeric type.
0.54
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.76
Mean of means among attributes of the numeric type.
0.25
Average mutual information between the nominal attributes and the target attribute.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.18
First quartile of means among attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.72
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
0.67
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

7 tasks

506 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: GG_new
303 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: GG_new
179 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: GG_new
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: GG_new
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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