Data
pasture

pasture

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

23 features

binaryClass (target)nominal2 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.
2
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.
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.
0.77
Average class difference between consecutive instances.
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.59
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.
0.59
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
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.
0.58
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
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
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.21
Mean kurtosis among attributes of the numeric type.
0.87
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.
0.22
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
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
211.37
Mean of means among attributes of the numeric type.
0.19
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.
0.59
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.58
Average mutual information between the nominal attributes and the target attribute.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.8
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.46
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.
0.58
First quartile of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.22
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
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.59
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
1.41
Standard deviation of the number of distinct values among attributes of the nominal type.
0.22
Kappa coefficient 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.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.25
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
66.67
Percentage of instances belonging to the most frequent class.
71.35
Mean standard deviation of attributes of the numeric type.
1.99
Second quartile (Median) of entropy among attributes.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.22
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
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
24
Number 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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Entropy of the target attribute values.
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.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.64
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.
0.58
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
2299.92
Maximum of means among attributes of the numeric type.
0.58
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.58
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
4.35
Percentage of binary attributes.
11.99
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.82
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.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1.59
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

5 tasks

483 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
215 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
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