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wdbc

wdbc

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  • cancer medical OpenML-CC18 OpenML100 study_123 study_135 study_14 study_52 study_7 study_98 study_99 uci study_225 study_236 study_293 study_253 study_275
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Author: William H. Wolberg, W. Nick Street, Olvi L. Mangasarian 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: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC). 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 (benign (1) or malignant (2)). [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 (Original) Data Set with a different set of features, better known as [breast-w](https://www.openml.org/d/15). ### Feature description Ten real-valued features are computed for each of 3 cell nuclei, yielding a total of 30 descriptive features. See the papers below for more details on how they were computed. The 10 features (in order) are: a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1) ### 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.

31 features

Class (target)nominal2 unique values
0 missing
V16numeric541 unique values
0 missing
V30numeric535 unique values
0 missing
V29numeric500 unique values
0 missing
V28numeric492 unique values
0 missing
V27numeric539 unique values
0 missing
V26numeric529 unique values
0 missing
V25numeric411 unique values
0 missing
V24numeric544 unique values
0 missing
V23numeric514 unique values
0 missing
V22numeric511 unique values
0 missing
V21numeric457 unique values
0 missing
V20numeric545 unique values
0 missing
V19numeric498 unique values
0 missing
V18numeric507 unique values
0 missing
V17numeric533 unique values
0 missing
V1numeric456 unique values
0 missing
V15numeric547 unique values
0 missing
V14numeric528 unique values
0 missing
V13numeric533 unique values
0 missing
V12numeric519 unique values
0 missing
V11numeric540 unique values
0 missing
V10numeric499 unique values
0 missing
V9numeric432 unique values
0 missing
V8numeric542 unique values
0 missing
V7numeric537 unique values
0 missing
V6numeric537 unique values
0 missing
V5numeric474 unique values
0 missing
V4numeric539 unique values
0 missing
V3numeric522 unique values
0 missing
V2numeric479 unique values
0 missing
Show all 31 features

107 properties

NumberOfInstances
569
Number of instances (rows) of the dataset.
NumberOfFeatures
31
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
30
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.63
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.87
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
0.8
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
0.05
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
CfsSubsetEval_NaiveBayesKappa
0.89
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
CfsSubsetEval_kNN1NAUC
0.95
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
CfsSubsetEval_kNN1NErrRate
0.05
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
CfsSubsetEval_kNN1NKappa
0.89
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
ClassEntropy
0.95
Entropy of the target attribute values.
DecisionStumpAUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.05
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
62.74
Percentage of instances belonging to the most frequent class.
MajorityClassSize
357
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
49.21
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
880.58
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
5.45
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
569.36
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
7.81
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
61.89
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.74
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
34.9
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.54
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.42
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
37.26
Percentage of instances belonging to the least frequent class.
MinorityClassSize
212
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
3.23
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
96.77
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
3.23
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.97
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.06
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.98
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.02
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
3.02
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.22
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.42
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
5.99
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
17.02
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.98
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
4.43
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

28 tasks

Supervised Classification on wdbc
147945 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on wdbc
76354 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on wdbc
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
Supervised Classification on wdbc
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on wdbc
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Supervised Classification on wdbc
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
Supervised Classification on wdbc
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Class
Learning Curve on wdbc
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
Clustering on wdbc
0 runs - target_feature: Class
Clustering on wdbc
0 runs
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Clustering on wdbc
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on wdbc
1297 runs - target_feature: Class
Subgroup Discovery on wdbc
1296 runs - target_feature: Class
Subgroup Discovery on wdbc
0 runs - target_feature: Class
Subgroup Discovery on wdbc
0 runs - target_feature: Class
Subgroup Discovery on wdbc
0 runs - target_feature: Class
Subgroup Discovery on wdbc
0 runs - target_feature: Class
Subgroup Discovery on wdbc
0 runs - target_feature: Class
Subgroup Discovery on wdbc
0 runs - target_feature: Class
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