Data
codrnaNorm

codrnaNorm

active Sparse_ARFF Publicly available Visibility: public Uploaded 14-02-2015 by Jan van Rijn
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Normalized form of codrna (351) Author: Andrew V Uzilov","Joshua M Keegan","David H Mathews. Source: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets) - Please cite: [AVU06a] Andrew V Uzilov, Joshua M Keegan, and David H Mathews. Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change. BMC Bioinformatics, 7(173), 2006. This is the cod-rna dataset, retrieved 2014-11-14 from the libSVM site. Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows: -join test, train and rest datasets -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. NOTE: please keep in mind that cod-rna has many duplicated data points, within each file (train,test,rest) and also accross these files. these duplicated points have not been removed!

9 features

codrna_Y (target)nominal2 unique values
0 missing
codrna_X1numeric1327 unique values
0 missing
codrna_X2numeric43 unique values
0 missing
codrna_X3numeric228 unique values
0 missing
codrna_X4numeric228 unique values
0 missing
codrna_X5numeric220 unique values
0 missing
codrna_X6numeric228 unique values
0 missing
codrna_X7numeric228 unique values
0 missing
codrna_X8numeric220 unique values
0 missing

107 properties

488565
Number of instances (rows) of the dataset.
9
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.
8
Number of numeric attributes.
1
Number of nominal attributes.
1.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Entropy of the target attribute values.
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
325710
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.38
Second quartile (Median) of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.17
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4.3
Maximum kurtosis among attributes of the numeric type.
0.31
Minimum of means among attributes of the numeric type.
0.89
Second quartile (Median) of skewness among attributes of the numeric type.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.78
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
11.11
Percentage of binary attributes.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.96
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.
0.04
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.
-1.08
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
3.59
Third quartile of kurtosis among attributes of the numeric type.
0.56
Average class difference between consecutive instances.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.65
Maximum skewness among attributes of the numeric type.
0.09
Minimum standard deviation of attributes of the numeric type.
88.89
Percentage of numeric attributes.
0.65
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.3
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
162855
Number of instances belonging to the least frequent class.
11.11
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.25
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.47
Third quartile of skewness among attributes of the numeric type.
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
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.01
Mean kurtosis among attributes of the numeric type.
0.26
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.13
First quartile of kurtosis among attributes of the numeric type.
0.16
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.47
Mean of means among attributes of the numeric type.
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.32
First quartile of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.97
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.
-0.9
First quartile of skewness among attributes of the numeric type.
0.91
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.
0.04
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.
0.14
First quartile of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.25
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.42
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
66.67
Percentage of instances belonging to the most frequent class.
0.16
Mean standard deviation of attributes of the numeric type.

5 tasks

0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: codrna_Y
309 runs - estimation_procedure: Interleaved Test then Train - target_feature: codrna_Y
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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