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cloud

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active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: Data from StatLib (ftp stat.cmu.edu/datasets) These data are those collected in a cloud-seeding experiment in Tasmania between mid-1964 and January 1971. Their analysis, using regression techniques and permutation tests, is discussed in: Miller, A.J., Shaw, D.E., Veitch, L.G. & Smith, E.J. (1979). `Analyzing the results of a cloud-seeding experiment in Tasmania', Communications in Statistics - Theory & Methods, vol.A8(10), 1017-1047. The rainfalls are period rainfalls in inches. TE and TW are the east and west target areas respectively, while NC, SC and NWC are the corresponding rainfalls in the north, south and north-west control areas respectively. S = seeded, U = unseeded. Rain in eastern target region is being treated as the class attribute. (Attribute for rain in the western target region has been deleted.)

6 features

TE (target)numeric94 unique values
0 missing
period (row identifier)numeric108 unique values
0 missing
seedednominal2 unique values
0 missing
seasonnominal4 unique values
0 missing
NCnumeric97 unique values
0 missing
SCnumeric89 unique values
0 missing
NWCnumeric101 unique values
0 missing

107 properties

108
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
0
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.
4
Number of numeric attributes.
2
Number of nominal attributes.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
33.33
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.78
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.14
Third quartile of skewness among attributes of the numeric type.
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.53
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.77
First quartile of kurtosis among attributes of the numeric type.
1.58
Third quartile of standard deviation of attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.21
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
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.
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
1.02
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.56
Mean skewness among attributes of the numeric type.
0.94
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.26
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
3
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
0.71
Minimum kurtosis among attributes of the numeric type.
1.37
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
8.4
Maximum kurtosis among attributes of the numeric type.
1.2
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
2.19
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.54
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
16.67
Percentage of binary attributes.
1.25
Second quartile (Median) of standard deviation of attributes of the numeric type.
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.
4
The maximum number of distinct values among attributes of the nominal type.
0.98
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.2
Maximum skewness among attributes of the numeric type.
0.89
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
7.56
Third quartile of kurtosis among attributes of the numeric type.
-0.01
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.63
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
66.67
Percentage of numeric attributes.
2.02
Third quartile of means among attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001

7 tasks

0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: TE
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: TE
0 runs - estimation_procedure: 33% Holdout set - target_feature: TE
0 runs - estimation_procedure: Leave one out - target_feature: TE
0 runs - estimation_procedure: Test on Training Data - target_feature: TE
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: TE
0 runs - estimation_procedure: 10% Holdout set - target_feature: TE
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