Data
breast-w

breast-w

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • cancer medical mythbusting_1 OpenML-CC18 OpenML100 study_1 study_123 study_135 study_14 study_15 study_20 study_34 study_37 study_41 study_50 study_52 study_70 study_98 study_99 uci
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Author: Dr. William H. Wolberg, University of Wisconsin Source: [UCI](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)), [University of Wisconsin](http://pages.cs.wisc.edu/~olvi/uwmp/cancer.html) - 1995 Please cite: See below, plus [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Breast Cancer Wisconsin (Original) Data Set. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. The target feature records the prognosis (malignant or benign). [Original data available here](ftp://ftp.cs.wisc.edu/math-prog/cpo-dataset/machine-learn/cancer/) Current dataset was adapted to ARFF format from the UCI version. Sample code ID's were removed. ! Note that there is also a related Breast Cancer Wisconsin (Diagnosis) Data Set with a different set of features, better known as [wdbc](https://www.openml.org/d/1510). ### Relevant Papers W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993. O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995. ### Citation request This breast cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. If you publish results when using this database, then please include this information in your acknowledgments. Also, please cite one or more of: 1. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. 2. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. 3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition via linear programming: Theory and application to medical diagnosis", in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30. 4. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).

10 features

Class (target)nominal2 unique values
0 missing
Clump_Thicknessnumeric10 unique values
0 missing
Cell_Size_Uniformitynumeric10 unique values
0 missing
Cell_Shape_Uniformitynumeric10 unique values
0 missing
Marginal_Adhesionnumeric10 unique values
0 missing
Single_Epi_Cell_Sizenumeric10 unique values
0 missing
Bare_Nucleinumeric10 unique values
16 missing
Bland_Chromatinnumeric10 unique values
0 missing
Normal_Nucleolinumeric10 unique values
0 missing
Mitosesnumeric9 unique values
0 missing

107 properties

699
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
16
Number of missing values in the dataset.
16
Number of instances with at least one value missing.
9
Number of numeric attributes.
1
Number of nominal attributes.
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.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.91
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
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
1.04
First quartile of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
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.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.48
Mean skewness among attributes of the numeric type.
2.33
First quartile of standard deviation of attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.04
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.75
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.9
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
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
65.52
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.18
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.93
Entropy of the target attribute values.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
458
Number of instances belonging to the most frequent class.
-0.8
Minimum kurtosis among attributes of the numeric type.
3.21
Second quartile (Median) of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
1.59
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
12.66
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.23
Second quartile (Median) of skewness among attributes of the numeric type.
0.88
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
4.42
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
10
Percentage of binary attributes.
2.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.89
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.
0.59
Minimum skewness among attributes of the numeric type.
2.29
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.11
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.
1.72
Minimum standard deviation of attributes of the numeric type.
0.23
Percentage of missing values.
1.58
Third quartile of kurtosis among attributes of the numeric type.
0.63
Average class difference between consecutive instances.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.56
Maximum skewness among attributes of the numeric type.
34.48
Percentage of instances belonging to the least frequent class.
90
Percentage of numeric attributes.
3.49
Third quartile of means among attributes of the numeric type.
0.92
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
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.64
Maximum standard deviation of attributes of the numeric type.
241
Number of instances belonging to the least frequent class.
10
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.09
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.62
Third quartile of skewness among attributes of the numeric type.
0.81
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
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.68
Mean kurtosis among attributes of the numeric type.
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.31
First quartile of kurtosis among attributes of the numeric type.
3.05
Third quartile of standard deviation of attributes of the numeric type.
0.99
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.14
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.84
First quartile of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.04
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

21 tasks

18412 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
334 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
321 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
216 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
355 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
208 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
0 runs - target_feature: Class
1305 runs - target_feature: Class
1301 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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