mammography

active ARFF Publicly available Visibility: public Uploaded 25-08-2014 by Tobias Kuehn
3 likes downloaded by 21 people , 25 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Please cite: Mammography dataset Past Usage: 1. Woods, K., Doss, C., Bowyer, K., Solka, J., Priebe, C.,

7 features

class (target)nominal2 unique values
0 missing
attr1numeric5435 unique values
0 missing
attr2numeric883 unique values
0 missing
attr3numeric160 unique values
0 missing
attr4numeric2800 unique values
0 missing
attr5numeric1739 unique values
0 missing
attr6numeric550 unique values
0 missing

102 properties

9
Stream landmarker
0
Stream landmarker
10
Stream landmarker
11
Stream landmarker
11382.8
DataQuality extracted from Fantail Library
0
DataQuality extracted from Fantail Library
2.32
DataQuality extracted from Fantail Library
6
Stream landmarker
0
Stream landmarker
1
Stream landmarker
6
Stream landmarker
0.24
DataQuality extracted from Fantail Library
1.39
DataQuality extracted from Fantail Library
2.9
DataQuality extracted from Fantail Library
20.23
DataQuality extracted from Fantail Library
0.84
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
0.02
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
0.54
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
0.84
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
0.02
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
0.54
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
0.84
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
0.02
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
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
0.16
Entropy of the target attribute values.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.02
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.98
The predictive accuracy obtained by always predicting the majority class.
0
Number of attributes divided by the number of instances.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
97.68
Percentage of instances belonging to the most frequent class.
10923
Number of instances belonging to the most frequent class.
163.26
Maximum kurtosis among attributes of the numeric type.
1409.62
Maximum of means among attributes of the numeric type.
-1
The maximum number of distinct values among attributes of the nominal type.
8.89
Maximum skewness among attributes of the numeric type.
-1
Average entropy of the attributes.
14.63
Mean kurtosis among attributes of the numeric type.
65.43
Mean of means among attributes of the numeric type.
-1
Average number of distinct values among the attributes of the nominal type.
1.83
Mean skewness among attributes of the numeric type.
312.47
Mean standard deviation of attributes of the numeric type.
-1.81
Minimum kurtosis among attributes of the numeric type.
-29
Minimum of means among attributes of the numeric type.
-1
The minimal number of distinct values among attributes of the nominal type.
-5.71
Minimum skewness among attributes of the numeric type.
260
Number of instances belonging to the least frequent class.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
Number of binary attributes.
2
Number of distinct values of the target attribute (if it is nominal).
7
Number of attributes (columns) of the dataset.
11183
Number of instances (rows) of the dataset.
0
Number of instances with at least one value missing.
0
Number of missing values in the dataset.
6
Number of numeric attributes.
1
Number of nominal attributes.
14.29
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
85.71
Percentage of numeric attributes.
14.29
Percentage of nominal attributes.
-0.43
First quartile of kurtosis among attributes of the numeric type.
0.4
First quartile of means among attributes of the numeric type.
0
First quartile of skewness among attributes of the numeric type.
0.69
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.8
Second quartile (Median) of means among attributes of the numeric type.
0.78
Second quartile (Median) of skewness among attributes of the numeric type.
13.87
Third quartile of kurtosis among attributes of the numeric type.
26.45
Third quartile of means among attributes of the numeric type.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.53
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.RandomTree -depth 2
0.02
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.53
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.RandomTree -depth 3
0.02
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Standard deviation of the number of distinct values among attributes of the nominal type.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.02
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

6 tasks

120 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
Define a new task

Discussions

Loading discussions...