Data
covertype

covertype

active Sparse_ARFF Publicly available Visibility: public Uploaded 15-08-2014 by aydin demircioglu
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Author: Jock A. Blackard, Dr. Denis J. Dean, Dr. Charles W. Anderson Source: [LibSVM repository](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/) - 2013-11-14 Please cite: For the binarization: R. Collobert, S. Bengio, and Y. Bengio. A parallel mixture of SVMs for very large scale problems. Neural Computation, 14(05):1105-1114, 2002. This is the famous covertype dataset in its binary version, retrieved 2013-11-13 from the libSVM site (called covtype.binary there). Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows: -load covertpype dataset, unscaled. -normalize each file columnwise according to the following rules: -If a column only contains one value (constant feature), it will set to zero and thus removed by sparsity. -If a column contains two values (binary feature), the value occuring more often will be set to zero, the other to one. -If a column contains more than two values (multinary/real feature), the column is divided by its std deviation. -duplicate lines were finally removed. Preprocessing: Transform from multiclass into binary class.

55 features

Y (target)nominal2 unique values
0 missing
X1numeric1978 unique values
0 missing
X2numeric361 unique values
0 missing
X3numeric67 unique values
0 missing
X4numeric551 unique values
0 missing
X5numeric700 unique values
0 missing
X6numeric5785 unique values
0 missing
X7numeric207 unique values
0 missing
X8numeric185 unique values
0 missing
X9numeric255 unique values
0 missing
X10numeric5827 unique values
0 missing
X11numeric2 unique values
0 missing
X12numeric2 unique values
0 missing
X13numeric2 unique values
0 missing
X14numeric2 unique values
0 missing
X15numeric2 unique values
0 missing
X16numeric2 unique values
0 missing
X17numeric2 unique values
0 missing
X18numeric2 unique values
0 missing
X19numeric2 unique values
0 missing
X20numeric2 unique values
0 missing
X21numeric2 unique values
0 missing
X22numeric2 unique values
0 missing
X23numeric2 unique values
0 missing
X24numeric2 unique values
0 missing
X25numeric2 unique values
0 missing
X26numeric2 unique values
0 missing
X27numeric2 unique values
0 missing
X28numeric2 unique values
0 missing
X29numeric2 unique values
0 missing
X30numeric2 unique values
0 missing
X31numeric2 unique values
0 missing
X32numeric2 unique values
0 missing
X33numeric2 unique values
0 missing
X34numeric2 unique values
0 missing
X35numeric2 unique values
0 missing
X36numeric2 unique values
0 missing
X37numeric2 unique values
0 missing
X38numeric2 unique values
0 missing
X39numeric2 unique values
0 missing
X40numeric2 unique values
0 missing
X41numeric2 unique values
0 missing
X42numeric2 unique values
0 missing
X43numeric2 unique values
0 missing
X44numeric2 unique values
0 missing
X45numeric2 unique values
0 missing
X46numeric2 unique values
0 missing
X47numeric2 unique values
0 missing
X48numeric2 unique values
0 missing
X49numeric2 unique values
0 missing
X50numeric2 unique values
0 missing
X51numeric2 unique values
0 missing
X52numeric2 unique values
0 missing
X53numeric2 unique values
0 missing
X54numeric2 unique values
0 missing

107 properties

581012
Number of instances (rows) of the dataset.
55
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.
54
Number of numeric attributes.
1
Number of nominal attributes.
Second quartile (Median) of entropy among attributes.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.48
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
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
51.24
Percentage of instances belonging to the most frequent class.
0.3
Mean standard deviation of attributes of the numeric type.
39.47
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
297711
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.02
Second quartile (Median) of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.96
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
193667.33
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
6.44
Second quartile (Median) of skewness among attributes of the numeric type.
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
11.3
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.82
Percentage of binary attributes.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.11
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
The maximum number of distinct values among attributes of the nominal type.
-1.18
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
315.61
Third quartile of kurtosis among attributes of the numeric type.
0.97
Average class difference between consecutive instances.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
440.08
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
98.18
Percentage of numeric attributes.
0.12
Third quartile of means among attributes of the numeric type.
0.78
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1
Maximum standard deviation of attributes of the numeric type.
48.76
Percentage of instances belonging to the least frequent class.
1.82
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.26
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
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
283301
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
17.81
Third quartile of skewness among attributes of the numeric type.
0.48
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
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4013.34
Mean kurtosis among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
5.23
First quartile of kurtosis among attributes of the numeric type.
0.32
Third quartile of standard deviation of attributes of the numeric type.
0.78
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.81
Mean of means among attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.26
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
0.11
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.48
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
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.95
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.
1
Number of binary attributes.
2.46
First quartile of skewness among attributes of the numeric type.
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.78
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.06
First quartile of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.26
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
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
20.55
Mean skewness among attributes of the numeric type.

7 tasks

22 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Y
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Y
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Y
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Y
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task