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NELL

NELL

active Sparse_ARFF Publicly available Visibility: public Uploaded 19-11-2015 by Andre Melo
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Nell HMC dataset for type prediction with ingoing/outgoing properties as features

769 features

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0 missing
735nominal2 unique values
0 missing
224nominal2 unique values
0 missing
480nominal2 unique values
0 missing
736nominal2 unique values
0 missing
225nominal2 unique values
0 missing
481nominal2 unique values
0 missing
737nominal2 unique values
0 missing
226nominal2 unique values
0 missing
482nominal2 unique values
0 missing
738nominal2 unique values
0 missing
227nominal2 unique values
0 missing
483nominal2 unique values
0 missing
739nominal2 unique values
0 missing
228nominal2 unique values
0 missing
484nominal2 unique values
0 missing
740nominal2 unique values
0 missing
229nominal2 unique values
0 missing
485nominal2 unique values
0 missing
741nominal2 unique values
0 missing
230nominal2 unique values
0 missing
486nominal2 unique values
0 missing
742nominal2 unique values
0 missing
231nominal2 unique values
0 missing
487nominal2 unique values
0 missing
743nominal2 unique values
0 missing
232nominal2 unique values
0 missing
488nominal2 unique values
0 missing
744nominal2 unique values
0 missing
233nominal2 unique values
0 missing
489nominal2 unique values
0 missing
745nominal2 unique values
0 missing
234nominal2 unique values
0 missing
490nominal2 unique values
0 missing
746nominal2 unique values
0 missing
235nominal1 unique values
0 missing
491nominal2 unique values
0 missing
747nominal2 unique values
0 missing
236nominal2 unique values
0 missing
492nominal2 unique values
0 missing
748nominal2 unique values
0 missing
237nominal2 unique values
0 missing
493nominal2 unique values
0 missing
749nominal2 unique values
0 missing
238nominal2 unique values
0 missing
494nominal2 unique values
0 missing
750nominal2 unique values
0 missing
239nominal2 unique values
0 missing
495nominal2 unique values
0 missing
751nominal2 unique values
0 missing
240nominal2 unique values
0 missing
496nominal2 unique values
0 missing
752nominal2 unique values
0 missing
241nominal2 unique values
0 missing
497nominal2 unique values
0 missing
753nominal2 unique values
0 missing
242nominal2 unique values
0 missing
498nominal2 unique values
0 missing
754nominal2 unique values
0 missing
243nominal2 unique values
0 missing
499nominal2 unique values
0 missing
755nominal2 unique values
0 missing
244nominal2 unique values
0 missing
500nominal2 unique values
0 missing
756nominal2 unique values
0 missing
245nominal2 unique values
0 missing
501nominal2 unique values
0 missing
757nominal2 unique values
0 missing
246nominal2 unique values
0 missing
502nominal2 unique values
0 missing
758nominal2 unique values
0 missing
247nominal2 unique values
0 missing
503nominal2 unique values
0 missing
759nominal2 unique values
0 missing
248nominal2 unique values
0 missing
504nominal2 unique values
0 missing
760nominal2 unique values
0 missing
249nominal2 unique values
0 missing
505nominal2 unique values
0 missing
761nominal2 unique values
0 missing
250nominal2 unique values
0 missing
506nominal2 unique values
0 missing
762nominal2 unique values
0 missing
251nominal2 unique values
0 missing
507nominal2 unique values
0 missing
763nominal2 unique values
0 missing
252nominal2 unique values
0 missing
508nominal2 unique values
0 missing
764nominal2 unique values
0 missing
253nominal2 unique values
0 missing
509nominal2 unique values
0 missing
765nominal2 unique values
0 missing
254nominal2 unique values
0 missing
510nominal2 unique values
0 missing
766nominal2 unique values
0 missing
255nominal2 unique values
0 missing
511nominal2 unique values
0 missing
767nominal2 unique values
0 missing

107 properties

120720
Number of instances (rows) of the dataset.
769
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.
0
Number of numeric attributes.
769
Number of nominal attributes.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
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.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
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
Maximum 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.
Average class difference between consecutive instances.
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
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
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
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.
100
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
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
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.
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
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
Mean of means among attributes of the numeric type.
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.
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
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
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
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.
769
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
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
0.12
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
1.99
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Mean skewness among attributes of the numeric type.
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
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.
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.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.
Second quartile (Median) of kurtosis 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.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
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 mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.

11 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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