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eucalyptus

eucalyptus

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Author: Bruce Bulloch Source: [WEKA Dataset Collection](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) - part of the agridatasets archive. [This is the true source](http://tunedit.org/repo/Data/Agricultural/eucalyptus.arff) Please cite: None Eucalyptus Soil Conservation The objective was to determine which seedlots in a species are best for soil conservation in seasonally dry hill country. Determination is found by measurement of height, diameter by height, survival, and other contributing factors. It is important to note that eucalypt trial methods changed over time; earlier trials included mostly 15 - 30cm tall seedling grown in peat plots and the later trials have included mostly three replications of eight trees grown. This change may contribute to less significant results. Experimental data recording procedures which require noting include: - instances with no data recorded due to experimental recording procedures require that the absence of a species from one replicate at a site was treated as a missing value, but if absent from two or more replicates at a site the species was excluded from the site's analyses. - missing data for survival, vigour, insect resistance, stem form, crown form and utility especially for the data recorded at the Morea Station; this could indicate the death of species in these areas or a lack in collection of data. ### Attribute Information 1. Abbrev - site abbreviation - enumerated 2. Rep - site rep - integer 3. Locality - site locality in the North Island - enumerated 4. Map_Ref - map location in the North Island - enumerated 5. Latitude - latitude approximation - enumerated 6. Altitude - altitude approximation - integer 7. Rainfall - rainfall (mm pa) - integer 8. Frosts - frosts (deg. c) - integer 9. Year - year of planting - integer 10. Sp - species code - enumerated 11. PMCno - seedlot number - integer 12. DBH - best diameter base height (cm) - real 13. Ht - height (m) - real 14. Surv - survival - integer 15. Vig - vigour - real 16. Ins_res - insect resistance - real 17. Stem_Fm - stem form - real 18. Crown_Fm - crown form - real 19. Brnch_Fm - branch form - real Class: 20. Utility - utility rating - enumerated ### Relevant papers Bulluch B. T., (1992) Eucalyptus Species Selection for Soil Conservation in Seasonally Dry Hill Country - Twelfth Year Assessment New Zealand Journal of Forestry Science 21(1): 10 - 31 (1991) Kirsten Thomson and Robert J. McQueen (1996) Machine Learning Applied to Fourteen Agricultural Datasets. University of Waikato Research Report https://www.cs.waikato.ac.nz/ml/publications/1996/Thomson-McQueen-96.pdf + the original publication:

20 features

Utility (target)nominal5 unique values
0 missing
PMCnonumeric85 unique values
7 missing
Brnch_Fmnumeric28 unique values
69 missing
Crown_Fmnumeric29 unique values
69 missing
Stem_Fmnumeric26 unique values
69 missing
Ins_resnumeric28 unique values
69 missing
Vignumeric33 unique values
69 missing
Survnumeric47 unique values
94 missing
Htnumeric531 unique values
1 missing
DBHnumeric603 unique values
1 missing
Abbrevnominal16 unique values
0 missing
Spnominal27 unique values
0 missing
Yearnumeric5 unique values
0 missing
Frostsnumeric2 unique values
0 missing
Rainfallnumeric10 unique values
0 missing
Altitudenumeric9 unique values
0 missing
Latitudenominal12 unique values
0 missing
Map_Refnominal14 unique values
0 missing
Localitynominal8 unique values
0 missing
Repnumeric4 unique values
0 missing
Show all 20 features

107 properties

NumberOfInstances
736
Number of instances (rows) of the dataset.
NumberOfFeatures
20
Number of attributes (columns) of the dataset.
NumberOfClasses
5
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
448
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
95
Number of instances with at least one value missing.
NumberOfNumericFeatures
14
Number of numeric attributes.
NumberOfSymbolicFeatures
6
Number of nominal attributes.
AutoCorrelation
0.39
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.82
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
CfsSubsetEval_DecisionStumpErrRate
0.42
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
CfsSubsetEval_DecisionStumpKappa
0.46
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
CfsSubsetEval_NaiveBayesAUC
0.82
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
CfsSubsetEval_NaiveBayesErrRate
0.42
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
CfsSubsetEval_NaiveBayesKappa
0.46
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
CfsSubsetEval_kNN1NAUC
0.82
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
CfsSubsetEval_kNN1NErrRate
0.42
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
CfsSubsetEval_kNN1NKappa
0.46
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
ClassEntropy
2.26
Entropy of the target attribute values.
DecisionStumpAUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.51
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.03
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
5.93
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
29.08
Percentage of instances belonging to the most frequent class.
MajorityClassSize
214
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.24
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
734.94
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
2054.74
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.43
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
27
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
27.11
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1551.78
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
3.46
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
62.87
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
390.09
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.38
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
8.08
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
13.67
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
2.55
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
172.61
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
2.58
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.89
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-2.58
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.25
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
5
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.7
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.49
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
14.27
Percentage of instances belonging to the least frequent class.
MinorityClassSize
105
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.45
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
12.91
Percentage of instances having missing values.
PercentageOfMissingValues
3.04
Percentage of missing values.
PercentageOfNumericFeatures
70
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
30
Percentage of nominal attributes.
Quartile1AttributeEntropy
2.91
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.5
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
2.88
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.32
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.4
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.78
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
3.48
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.43
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
6.25
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.41
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.11
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1.35
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
4.01
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
1.36
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
403
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.43
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.95
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
80.62
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.56
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.56
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.56
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
7.66
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.47
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

41 tasks

Supervised Classification on eucalyptus
20195 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Utility
Supervised Classification on eucalyptus
321 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Utility
Supervised Classification on eucalyptus
179 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Utility
Supervised Classification on eucalyptus
0 runs - estimation_procedure: 33% Holdout set - target_feature: Utility
Supervised Classification on eucalyptus
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Utility
Supervised Classification on eucalyptus
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Utility
Learning Curve on eucalyptus
359 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Utility
Supervised Data Stream Classification on eucalyptus
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Utility
Supervised Data Stream Classification on eucalyptus
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Utility
Clustering on eucalyptus
0 runs - target_feature: Utility
Clustering on eucalyptus
0 runs
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Clustering on eucalyptus
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on eucalyptus
1312 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
1310 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
1310 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
1305 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
1304 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
Subgroup Discovery on eucalyptus
0 runs - target_feature: Utility
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