Data
wind_correlations

wind_correlations

active ARFF Publicly available Visibility: public Uploaded 29-09-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: These data are estimated correlations between daily 3 p.m. wind measurements during September and October 1997 for a network of 45 stations in the Sydney region. The first column below gives a list of station latitudes, the second gives a list of station longitudes, and the remaining 45 columns give the 45 x 45 spatial correlation matrix of the station measurements. Further details about the data are contained in the following technical report: Nott and Dunsmuir (1998) ``Analysis of Spatial Covariance Structure from Monitoring Data,'' Technical Report, Department of Statistics, University of New South Wales. Email djn@maths.unsw.edu.au with any questions or to obtain a copy of the latest version of the above report. Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

47 features

latitudenumeric43 unique values
0 missing
longitudenumeric44 unique values
0 missing
station_1numeric44 unique values
0 missing
station_2numeric45 unique values
0 missing
station_3numeric44 unique values
0 missing
station_4numeric44 unique values
0 missing
station_5numeric44 unique values
0 missing
station_6numeric43 unique values
0 missing
station_7numeric42 unique values
0 missing
station_8numeric41 unique values
0 missing
station_9numeric43 unique values
0 missing
station_10numeric44 unique values
0 missing
station_11numeric44 unique values
0 missing
station_12numeric45 unique values
0 missing
station_13numeric42 unique values
0 missing
station_14numeric43 unique values
0 missing
station_15numeric43 unique values
0 missing
station_16numeric43 unique values
0 missing
station_17numeric45 unique values
0 missing
station_18numeric45 unique values
0 missing
station_19numeric45 unique values
0 missing
station_20numeric45 unique values
0 missing
station_21numeric44 unique values
0 missing
station_22numeric44 unique values
0 missing
station_23numeric40 unique values
0 missing
station_24numeric43 unique values
0 missing
station_25numeric43 unique values
0 missing
station_26numeric44 unique values
0 missing
station_27numeric44 unique values
0 missing
station_28numeric44 unique values
0 missing
station_29numeric45 unique values
0 missing
station_30numeric45 unique values
0 missing
station_31numeric44 unique values
0 missing
station_32numeric44 unique values
0 missing
station_33numeric44 unique values
0 missing
station_34numeric45 unique values
0 missing
station_35numeric44 unique values
0 missing
station_36numeric44 unique values
0 missing
station_37numeric44 unique values
0 missing
station_38numeric43 unique values
0 missing
station_39numeric45 unique values
0 missing
station_40numeric43 unique values
0 missing
station_41numeric44 unique values
0 missing
station_42numeric42 unique values
0 missing
station_43numeric41 unique values
0 missing
station_44numeric45 unique values
0 missing
station_45numeric44 unique values
0 missing

107 properties

45
Number of instances (rows) of the dataset.
47
Number of attributes (columns) of the dataset.
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.
47
Number of numeric attributes.
0
Number of nominal attributes.
0.18
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
150.88
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.19
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
1.04
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
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.
The maximum number of distinct values among attributes of the nominal type.
-1.17
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.71
Third quartile of kurtosis among attributes of the numeric type.
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
2.62
Maximum skewness among attributes of the numeric type.
0.13
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.55
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
0.53
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric 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
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0
Percentage of nominal attributes.
0.3
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
1.12
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.
0.23
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
2.94
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.02
First quartile of kurtosis 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
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.41
First quartile of means among attributes of the numeric type.
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
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.
-0.17
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
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
Average number of distinct values among the attributes of the nominal type.
0.16
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
0.17
Mean skewness among 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.
0.21
Mean standard deviation of attributes of the numeric type.
0.47
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.
Minimal entropy among attributes.
0.48
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
Maximum entropy among attributes.
-0.75
Minimum kurtosis 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
12.99
Maximum kurtosis among attributes of the numeric type.
-33.86
Minimum of means among attributes of the numeric type.

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