Data
pasture

pasture

active ARFF Publicly available Visibility: public Uploaded 26-08-2014 by Joaquin Vanschoren
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Author: Dave Barker Source: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) - Please cite: Pasture Production Data source: Dave Barker AgResearch Grasslands, Palmerston North, New Zealand The objective was to predict pasture production from a variety of biophysical factors. Vegetation and soil variables from areas of grazed North Island hill country with different management (fertilizer application/stocking rate) histories (1973-1994) were measured and subdivided into 36 paddocks. Nineteen vegetation (including herbage production); soil chemical, physical and biological; and soil water variables were selected as potentially useful biophysical indicators. Attribute Information: 1. fertiliser - fertiliser used - enumerated 2. slope - slope of the paddock - integer 3. aspect-dev-NW - the deviation from the north-west - integer 4. OlsenP - integer 5. MinN - integer 6. TS - integer 7. Ca-Mg - calcium magnesium ration - real 8. LOM - soil lom (g/100g) - real 9. NFIX-mean - a mean calculation - real 10. Eworms-main-3 - main 3 spp earth worms per g/m2 - real 11. Eworms-No-species - number of spp - integer 12. KUnSat - mm/hr - real 13. OM - real 14. Air-Perm - real 15. Porosity - real 16. HFRG-pct-mean - mean percent - real 17. legume-yield - kgDM/ha - real 18. OSPP-pct-mean - mean percent - real 19. Jan-Mar-mean-TDR - real 20. Annual-Mean-Runoff - in mm - real 21. root-surface-area - m2/m3 - real 22. Leaf-P - ppm - real 23. pasture-prod-class - pasture production categorisation - enumerated

23 features

pasture-prod-class (target)nominal3 unique values
0 missing
fertilisernominal4 unique values
0 missing
slopenumeric20 unique values
0 missing
aspect-dev-NWnumeric31 unique values
0 missing
OlsenPnumeric18 unique values
0 missing
MinNnumeric35 unique values
0 missing
TSnumeric31 unique values
0 missing
Ca-Mgnumeric22 unique values
0 missing
LOMnumeric33 unique values
0 missing
NFIX-meannumeric36 unique values
0 missing
Eworms-main-3numeric34 unique values
0 missing
Eworms-No-speciesnumeric5 unique values
0 missing
KUnSatnumeric34 unique values
0 missing
OMnumeric25 unique values
0 missing
Air-Permnumeric1 unique values
0 missing
Porositynumeric29 unique values
0 missing
HFRG-pct-meannumeric36 unique values
0 missing
legume-yieldnumeric36 unique values
0 missing
OSPP-pct-meannumeric36 unique values
0 missing
Jan-Mar-mean-TDRnumeric34 unique values
0 missing
Annual-Mean-Runoffnumeric36 unique values
0 missing
root-surface-areanumeric35 unique values
0 missing
Leaf-Pnumeric36 unique values
0 missing

107 properties

36
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
3
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.
21
Number of numeric attributes.
2
Number of nominal attributes.
211.37
Mean of means among attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.75
First quartile of kurtosis among attributes of the numeric type.
75.9
Third quartile of standard deviation of attributes of the numeric type.
0.8
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.79
Average mutual information between the nominal attributes and the target attribute.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.8
First quartile of means among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
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
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.53
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.
0.79
First quartile of mutual information between the nominal attributes and the target attribute.
0.53
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.63
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
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.5
Average number of distinct values among the attributes of the nominal type.
0.22
First quartile of skewness among attributes of the numeric type.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.8
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
0.71
Standard deviation of the number of distinct values among attributes of the nominal type.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.51
Mean skewness among attributes of the numeric type.
1.03
First quartile of standard deviation of attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.25
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
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
71.35
Mean standard deviation of attributes of the numeric type.
1.99
Second quartile (Median) of entropy among attributes.
0.53
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.63
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
0.25
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
33.33
Percentage of instances belonging to the most frequent class.
1.99
Minimal entropy among attributes.
-0.07
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.58
Entropy of the target attribute values.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
12
Number of instances belonging to the most frequent class.
1.99
Maximum entropy among attributes.
-1.11
Minimum kurtosis among attributes of the numeric type.
21.4
Second quartile (Median) of means among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
5.79
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0.79
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.53
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.47
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
2299.92
Maximum of means among attributes of the numeric type.
0.79
Minimal mutual information between the nominal attributes and the target attribute.
0.46
Second quartile (Median) of skewness among attributes of the numeric type.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.79
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
11.99
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.64
Number of attributes divided by the number of instances.
4
The maximum number of distinct values among attributes of the nominal type.
-0.5
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.99
Third quartile of entropy among attributes.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
2.02
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.22
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.43
Third quartile of kurtosis among attributes of the numeric type.
0.6
Average class difference between consecutive instances.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
839.57
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
91.3
Percentage of numeric attributes.
208.98
Third quartile of means among attributes of the numeric type.
0.8
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.99
Average entropy of the attributes.
12
Number of instances belonging to the least frequent class.
8.7
Percentage of nominal attributes.
0.79
Third quartile of mutual information between the nominal attributes and the target attribute.
0.25
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
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.21
Mean kurtosis among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.99
First quartile of entropy among attributes.
0.82
Third quartile of skewness among attributes of the numeric type.
0.63
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
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

7 tasks

390 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: pasture-prod-class
309 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: pasture-prod-class
179 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: pasture-prod-class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: pasture-prod-class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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