Data
GCM

GCM

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Multiclass cancer diagnosis using 16063 tumor gene expression signatures. PNAS, VOL 98, no 26, pp. 15149-15154, December 18, 2001. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mesirov, T. Poggio, W. Gerald, M. Loda, E.S. Lander and T.R. Golub. Original split: 144 first instances for training, remainder for testing

16064 features

class (target)nominal14 unique values
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S57153_s_atnumeric103 unique values
0 missing
X53296_s_atnumeric149 unique values
0 missing
X72304_atnumeric105 unique values
0 missing
AFFX-HUMGAPDH/M33197_3_at-2numeric190 unique values
0 missing
AA090439_atnumeric176 unique values
0 missing
AA165144_f_atnumeric122 unique values
0 missing
AA248786_atnumeric138 unique values
0 missing
AA314256_atnumeric138 unique values
0 missing
AA416829_atnumeric104 unique values
0 missing
AA456687_atnumeric157 unique values
0 missing
AB002338_atnumeric129 unique values
0 missing
D31184_atnumeric165 unique values
0 missing
H19597_atnumeric141 unique values
0 missing
L20814_at-2numeric147 unique values
0 missing
N44971_atnumeric138 unique values
0 missing
R59531_atnumeric90 unique values
0 missing
T58486_atnumeric92 unique values
0 missing
U76343_atnumeric156 unique values
0 missing
W26496_atnumeric170 unique values
0 missing
W56613_atnumeric157 unique values
0 missing
X98330_at-2numeric111 unique values
0 missing
RC_AA020925_atnumeric104 unique values
0 missing
RC_AA055992_atnumeric166 unique values
0 missing
RC_AA134141_atnumeric160 unique values
0 missing
RC_AA194045_atnumeric170 unique values
0 missing
RC_AA235803_i_atnumeric177 unique values
0 missing
RC_AA256326_atnumeric156 unique values
0 missing
RC_AA282241_atnumeric168 unique values
0 missing
RC_AA382114_atnumeric159 unique values
0 missing

107 properties

190
Number of instances (rows) of the dataset.
16064
Number of attributes (columns) of the dataset.
14
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.
16063
Number of numeric attributes.
1
Number of nominal attributes.
0.67
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.81
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
188.11
Maximum kurtosis among attributes of the numeric type.
-6243.93
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
14439.27
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.92
Second quartile (Median) of skewness among attributes of the numeric type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
84.55
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
14
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
198.6
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.67
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.
14
The maximum number of distinct values among attributes of the nominal type.
-13.53
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.86
Average class difference between consecutive instances.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
13.68
Maximum skewness among attributes of the numeric type.
18.63
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
17.02
Third quartile of kurtosis among attributes of the numeric type.
0.7
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.56
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
8306.13
Maximum standard deviation of attributes of the numeric type.
5.26
Percentage of instances belonging to the least frequent class.
99.99
Percentage of numeric attributes.
272.3
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.57
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
0.67
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
10
Number of instances belonging to the least frequent class.
0.01
Percentage of nominal attributes.
3.21
Third quartile of skewness among attributes of the numeric type.
0.38
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
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
15.37
Mean kurtosis among attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
405.44
Third quartile of standard deviation of attributes of the numeric type.
0.7
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.56
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
242.48
Mean of means among attributes of the numeric type.
0.42
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.33
First quartile of kurtosis among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.57
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
0.67
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
9.12
First quartile of means among attributes of the numeric type.
0.67
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.38
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
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.7
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
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.7
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.
0.56
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
14
Average number of distinct values among the attributes of the nominal type.
0.82
First quartile of skewness among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.57
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.06
Mean skewness among attributes of the numeric type.
101.08
First quartile of standard deviation of attributes of the numeric type.
0.67
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.38
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
0.45
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
15.79
Percentage of instances belonging to the most frequent class.
382.67
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.7
Entropy of the target attribute values.
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
30
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
7.07
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.67
Minimum kurtosis among attributes of the numeric type.
94.15
Second quartile (Median) of means among attributes of the numeric type.

13 tasks

78 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
38 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - 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: 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: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task