Data
primary-tumor

primary-tumor

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.

18 features

binaryClass (target)nominal2 unique values
0 missing
agenominal3 unique values
0 missing
sexnominal2 unique values
1 missing
histologic-typenominal3 unique values
67 missing
degree-of-diffenominal3 unique values
155 missing
bonenominal2 unique values
0 missing
bone-marrownominal2 unique values
0 missing
lungnominal2 unique values
0 missing
pleuranominal2 unique values
0 missing
peritoneumnominal2 unique values
0 missing
livernominal2 unique values
0 missing
brainnominal2 unique values
0 missing
skinnominal2 unique values
1 missing
necknominal2 unique values
0 missing
supraclavicularnominal2 unique values
0 missing
axillarnominal2 unique values
1 missing
mediastinumnominal2 unique values
0 missing
abdominalnominal2 unique values
0 missing

108 properties

339
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
225
Number of missing values in the dataset.
207
Number of instances with at least one value missing.
0
Number of numeric attributes.
18
Number of nominal attributes.
0
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
255
Number of instances belonging to the most frequent class.
0
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.23
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Second quartile (Median) of skewness among attributes of the numeric type.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
The predictive accuracy obtained by always predicting the majority class.
0
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0
DataQuality extracted from Fantail Library
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.05
Number of attributes divided by the number of instances.
0.18
Maximum mutual information between the nominal attributes and the target attribute.
0
Minimum skewness among attributes of the numeric type.
83.33
Percentage of binary attributes.
61.06
Percentage of instances having missing values.
0.96
Third quartile of entropy among attributes.
0.61
Average class difference between consecutive instances.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
22.56
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3
The maximum number of distinct values among attributes of the nominal type.
0
DataQuality extracted from Fantail Library
3.69
Percentage of missing values.
0
Third quartile of kurtosis among attributes of the numeric type.
0.75
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Maximum skewness among attributes of the numeric type.
0.25
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
0.22
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
DataQuality extracted from Fantail Library
84
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
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
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.76
Average entropy of the attributes.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.51
First quartile of entropy among attributes.
0
DataQuality extracted from Fantail Library
0.75
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Mean kurtosis among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of kurtosis among attributes of the numeric type.
0
DataQuality extracted from Fantail Library
0.22
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Mean of means among attributes of the numeric type.
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of means among attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.04
Average mutual information between the nominal attributes and the target attribute.
20.26
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.75
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.18
Average number of distinct values among the attributes of the nominal type.
15
Number of binary attributes.
0
First quartile of skewness among attributes of the numeric type.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.22
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
0.39
Standard deviation of the number of distinct values among attributes of the nominal type.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean skewness among attributes of the numeric type.
0
DataQuality extracted from Fantail Library
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean standard deviation of attributes of the numeric type.
0.84
Second quartile (Median) of entropy among attributes.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.81
Entropy of the target attribute values.
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
75.22
Percentage of instances belonging to the most frequent class.
0.15
Minimal entropy among attributes.

4 tasks

526 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
198 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
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