Data
molecular-biology_promoters

molecular-biology_promoters

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.

59 features

binaryClass (target)nominal2 unique values
0 missing
classnominal2 unique values
0 missing
instance (ignore)nominal106 unique values
0 missing
p-50nominal4 unique values
0 missing
p-49nominal4 unique values
0 missing
p-48nominal4 unique values
0 missing
p-47nominal4 unique values
0 missing
p-46nominal4 unique values
0 missing
p-45nominal4 unique values
0 missing
p-44nominal4 unique values
0 missing
p-43nominal4 unique values
0 missing
p-42nominal4 unique values
0 missing
p-41nominal4 unique values
0 missing
p-40nominal4 unique values
0 missing
p-39nominal4 unique values
0 missing
p-38nominal4 unique values
0 missing
p-37nominal4 unique values
0 missing
p-36nominal4 unique values
0 missing
p-35nominal4 unique values
0 missing
p-34nominal4 unique values
0 missing
p-33nominal4 unique values
0 missing
p-32nominal4 unique values
0 missing
p-31nominal4 unique values
0 missing
p-30nominal4 unique values
0 missing
p-29nominal4 unique values
0 missing
p-28nominal4 unique values
0 missing
p-27nominal4 unique values
0 missing
p-26nominal4 unique values
0 missing
p-25nominal4 unique values
0 missing
p-24nominal4 unique values
0 missing
p-23nominal4 unique values
0 missing
p-22nominal4 unique values
0 missing
p-21nominal4 unique values
0 missing
p-20nominal4 unique values
0 missing
p-19nominal4 unique values
0 missing
p-18nominal4 unique values
0 missing
p-17nominal4 unique values
0 missing
p-16nominal4 unique values
0 missing
p-15nominal4 unique values
0 missing
p-14nominal4 unique values
0 missing
p-13nominal4 unique values
0 missing
p-12nominal4 unique values
0 missing
p-11nominal4 unique values
0 missing
p-10nominal4 unique values
0 missing
p-9nominal4 unique values
0 missing
p-8nominal4 unique values
0 missing
p-7nominal4 unique values
0 missing
p-6nominal4 unique values
0 missing
p-5nominal4 unique values
0 missing
p-4nominal4 unique values
0 missing
p-3nominal4 unique values
0 missing
p-2nominal4 unique values
0 missing
p-1nominal4 unique values
0 missing
p1nominal4 unique values
0 missing
p2nominal4 unique values
0 missing
p3nominal4 unique values
0 missing
p4nominal4 unique values
0 missing
p5nominal4 unique values
0 missing
p6nominal4 unique values
0 missing

107 properties

106
Number of instances (rows) of the dataset.
59
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.
0
Number of numeric attributes.
59
Number of nominal attributes.
2
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-0.02
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
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
70.5
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
First quartile of skewness among attributes of the numeric type.
-0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.52
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
0.37
Standard deviation of the number of distinct values among attributes of the nominal type.
0.39
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.93
Average number of distinct values among the attributes of the nominal type.
First quartile of standard deviation of attributes of the numeric type.
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.39
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
1.96
Second quartile (Median) of entropy among attributes.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-0.02
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
0.43
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
67.92
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
-0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.91
Entropy of the target attribute values.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
72
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
Second quartile (Median) of means among attributes of the numeric type.
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.32
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
Second quartile (Median) of skewness among attributes of the numeric type.
-0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
3.39
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.56
Number of attributes divided by the number of instances.
0.08
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.
1.98
Third quartile of entropy among attributes.
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
33.54
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.56
Average class difference between consecutive instances.
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
32.08
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.52
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.39
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
34
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
0.39
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
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.93
Average entropy of the attributes.
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.93
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
-0.02
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
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.28
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.52
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.39
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.39
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
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.03
Average mutual information between the nominal attributes and the target attribute.

13 tasks

142 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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