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tumors_C

tumors_C

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Embryonal tumours of the central nervous system Prediction of Central Nervous System Embryonal Tumour Outcome based on Gene Expression. Nature, VOL 415, pp. 436-442, 24 January 2002. Scott L. Pomeroy, Pablo Tamayo, Michelle Gaasenbeek, Lisa M. Sturla, Michael Angelo, Margaret E. McLaughlin, John Y. H. Kim, Liliana C. Goumnerova, Peter M. Black, Ching Lau, Jeffrey C. Allen, David Zagzag, James M. Olson, Tom Curran, Cynthia Wetmore, Jaclyn A. Biegel, Tomaso Poggio, Shayan Mukherjee, Ryan Rifkin, Andrea Califano, Gustavo Stolovitzky, David N. Louis, Jill P. Mesirov, Eric S. Lander & Todd R. Golub

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0 missing
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0 missing
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M14328_s_atnumeric60 unique values
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0 missing
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M90656_atnumeric56 unique values
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0 missing
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0 missing
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0 missing
X13444_atnumeric59 unique values
0 missing
X63454_atnumeric54 unique values
0 missing
X80230_atnumeric56 unique values
0 missing

107 properties

60
Number of instances (rows) of the dataset.
7130
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.
7129
Number of numeric attributes.
1
Number of nominal attributes.
0.98
Average class difference between consecutive instances.
-0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
7.68
Maximum skewness among attributes of the numeric type.
48.04
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
5.48
Third quartile of kurtosis among attributes of the numeric type.
0.51
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
0.43
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
35115.37
Maximum standard deviation of attributes of the numeric type.
35
Percentage of instances belonging to the least frequent class.
99.99
Percentage of numeric attributes.
480.62
Third quartile of means among attributes of the numeric type.
0.4
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
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
21
Number of instances belonging to the least frequent class.
0.01
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.06
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
-0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4.7
Mean kurtosis among attributes of the numeric type.
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.55
Third quartile of skewness among attributes of the numeric type.
466.62
Third quartile of standard deviation of attributes of the numeric type.
0.51
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
0.43
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
531.33
Mean of means among attributes of the numeric type.
0.32
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.09
First quartile of kurtosis among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.4
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
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.59
First quartile of means among attributes of the numeric type.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.06
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
-0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.53
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.
First quartile of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.51
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.
0.4
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.
-0.25
First quartile of skewness among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.4
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
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.71
Mean skewness among attributes of the numeric type.
143.31
First quartile of standard deviation of attributes of the numeric type.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.06
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
0.48
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
65
Percentage of instances belonging to the most frequent class.
511.03
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.93
Entropy of the target attribute values.
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
39
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.6
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.09
Minimum kurtosis among attributes of the numeric type.
149.2
Second quartile (Median) of means among attributes of the numeric type.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
59.33
Maximum kurtosis among attributes of the numeric type.
-17105.73
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
40666.05
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.81
Second quartile (Median) of skewness among attributes of the numeric type.
0.43
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
118.83
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.
0.01
Percentage of binary attributes.
248.33
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.48
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.
-7.51
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.

6 tasks

263 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CLASS
80 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CLASS
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: CLASS
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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