Data
white-clover

white-clover

active ARFF Publicly available Visibility: public Uploaded 26-08-2014 by Joaquin Vanschoren
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Author: Ian Tarbotton Source: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) - Please cite: White Clover Persistence Trials Data source: Ian Tarbotton AgResearch, Whatawhata Research Centre, Hamilton, New Zealand The objective was to determine the mechanisms which influence the persistence of white clover populations in summer dry hill land. In particular reference to the consequence of a severe summer dry period in 1993/1994 and how it impacted on the performance of three white clover cultivars in an on-going experiment located at Whatawhata Research Centre. The machine learning objective was to predict the amount of white clover in 1994 from the amount of white clover and other species in the years 1991 to 1994 as well as information on the 'strata' where the white clover was being grown. Attribute Information: 1. strata - enumerated 2. plot - enumerated 3. paddock - enumerated 4. WhiteClover-91 - white clover measurement in 1991 - real 5. BareGround-91 - bare ground measurement in 1991 - real 6. Cocksfoot-91 - cocksfoot measurement in 1991 - real 7. OtherGrasses-91 - other grasses measurement in 1991 - real 8. OtherLegumes-91 - other legumes measurement in 1991 - real 9. RyeGrass-91 - ryegrass measurement in 1991 - real 10. Weeds-91 - weeds measurement in 1991 - real 11. WhiteClover-92 - white clover measurement in 1992 - real 12. BareGround-92 - bare ground measurement in 1992 - real 13. Cocksfoot-92 - cocksfoot measurement in 1992 - real 14. OtherGrasses-92 - other grasses measurement in 1992 - real 15. OtherLegumes-92 - other legumes measurement in 1992 - real 16. RyeGrass-92 - ryegrass measurement in 1992 - real 17. Weeds-92 - weeds measurement in 1992 - real 18. WhiteClover-93 - white clover measurement in 1993 - real 19. BareGround-93 - bare ground measurement in 1993 - real 20. Cocksfoot-93 - cocksfoot measurement in 1993 - real 21. OtherGrasses-93 - other grasses measurement in 1993 - real 22. OtherLegumes-93 - other legumes measurement in 1993 - real 23. RyeGrass-93 - ryegrass measurement in 1993 - real 24. Weeds-93 - weeds measurement in 1993 - real 25. BareGround-94 - bare ground measurement in 1994 - real 26. Cocksfoot-94 - cocksfoot measurement in 1994 - real 27. OtherGrasses-94 - other grasses measurement in 1994 - real 28. OtherLegumes-94 - other legumes measurement in 1994 - real 29. RyeGrass-94 - ryegrass measurement in 1994 - real 30. Weeds-94 - weeds measurement in 1994 - real 31. strata-combined - enumerated 32. WhiteClover-94 - white clover measurement in 1994 - enumerated

32 features

WhiteClover-94 (target)nominal4 unique values
0 missing
stratanominal7 unique values
0 missing
plotnominal3 unique values
0 missing
paddocknominal3 unique values
0 missing
WhiteClover-91numeric51 unique values
0 missing
BareGround-91numeric32 unique values
0 missing
Cocksfoot-91numeric50 unique values
0 missing
OtherGrasses-91numeric55 unique values
0 missing
OtherLegumes-91numeric36 unique values
0 missing
RyeGrass-91numeric58 unique values
0 missing
Weeds-91numeric51 unique values
0 missing
WhiteClover-92numeric51 unique values
0 missing
BareGround-92numeric31 unique values
0 missing
Cocksfoot-92numeric51 unique values
0 missing
OtherGrasses-92numeric42 unique values
0 missing
OtherLegumes-92numeric32 unique values
0 missing
RyeGrass-92numeric51 unique values
0 missing
Weeds-92numeric44 unique values
0 missing
WhiteClover-93numeric50 unique values
0 missing
BareGround-93numeric20 unique values
0 missing
Cocksfoot-93numeric53 unique values
0 missing
OtherGrasses-93numeric50 unique values
0 missing
OtherLegumes-93numeric40 unique values
0 missing
RyeGrass-93numeric52 unique values
0 missing
Weeds-93numeric51 unique values
0 missing
BareGround-94numeric23 unique values
0 missing
Cocksfoot-94numeric50 unique values
0 missing
OtherGrasses-94numeric45 unique values
0 missing
OtherLegumes-94numeric42 unique values
0 missing
RyeGrass-94numeric50 unique values
0 missing
Weeds-94numeric49 unique values
0 missing
strata-combinednominal3 unique values
0 missing

107 properties

63
Number of instances (rows) of the dataset.
32
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.
27
Number of numeric attributes.
5
Number of nominal attributes.
9.86
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.
0.08
First quartile of mutual information between the nominal attributes and the target attribute.
0.44
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
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
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
4
Average number of distinct values among the attributes of the nominal type.
0.35
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
1.73
Standard deviation of the number of distinct values among attributes of the nominal type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.28
Mean skewness among attributes of the numeric type.
5.38
First quartile of standard deviation of attributes of the numeric type.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.46
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
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
9.67
Mean standard deviation of attributes of the numeric type.
1.58
Second quartile (Median) of entropy among attributes.
0.44
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.01
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
0.49
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
60.32
Percentage of instances belonging to the most frequent class.
1.38
Minimal entropy among attributes.
0.96
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
1.31
Entropy of the target attribute values.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
38
Number of instances belonging to the most frequent class.
-1.04
Minimum kurtosis among attributes of the numeric type.
13.8
Second quartile (Median) of means among attributes of the numeric type.
0.56
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
2.81
Maximum entropy among attributes.
0.43
Minimum of means among attributes of the numeric type.
0.13
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.44
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.52
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
42.42
Maximum kurtosis among attributes of the numeric type.
0.08
Minimal mutual information between the nominal attributes and the target attribute.
0.68
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
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
30.52
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.
9.4
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.51
Number of attributes divided by the number of instances.
0.33
Maximum mutual information between the nominal attributes and the target attribute.
-0.44
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2.5
Third quartile of entropy among attributes.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
7.76
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.
1.09
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
4.78
Third quartile of kurtosis among attributes of the numeric type.
0.61
Average class difference between consecutive instances.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.09
Maximum skewness among attributes of the numeric type.
1.59
Percentage of instances belonging to the least frequent class.
84.38
Percentage of numeric attributes.
19.67
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
20.71
Maximum standard deviation of attributes of the numeric type.
1
Number of instances belonging to the least frequent class.
15.63
Percentage of nominal attributes.
0.3
Third quartile of mutual information between the nominal attributes and the target attribute.
0.46
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.84
Average entropy of the attributes.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.43
First quartile of entropy among attributes.
2.1
Third quartile of skewness among attributes of the numeric type.
0.01
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
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.17
Mean kurtosis among attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.32
First quartile of kurtosis among attributes of the numeric type.
13.41
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
13.59
Mean of means among attributes of the numeric type.
0.17
Average mutual information between the nominal attributes and the target attribute.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
5.65
First quartile of means among attributes of the numeric type.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.46
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
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

6 tasks

348 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: WhiteClover-94
317 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: WhiteClover-94
193 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: WhiteClover-94
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: WhiteClover-94
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