Data
wdbc

wdbc

active ARFF Publicly available Visibility: public Uploaded 26-05-2015 by Rafael G. Mantovani
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  • cancer medical OpenML-CC18 OpenML100 study_123 study_135 study_14 study_52 study_7 study_98 study_99 uci
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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
V1numeric456 unique values
0 missing
V2numeric479 unique values
0 missing
V3numeric522 unique values
0 missing
V4numeric539 unique values
0 missing
V5numeric474 unique values
0 missing
V6numeric537 unique values
0 missing
V7numeric537 unique values
0 missing
V8numeric542 unique values
0 missing
V9numeric432 unique values
0 missing
V10numeric499 unique values
0 missing
V11numeric540 unique values
0 missing
V12numeric519 unique values
0 missing
V13numeric533 unique values
0 missing
V14numeric528 unique values
0 missing
V15numeric547 unique values
0 missing
V16numeric541 unique values
0 missing
V17numeric533 unique values
0 missing
V18numeric507 unique values
0 missing
V19numeric498 unique values
0 missing
V20numeric545 unique values
0 missing
V21numeric457 unique values
0 missing
V22numeric511 unique values
0 missing
V23numeric514 unique values
0 missing
V24numeric544 unique values
0 missing
V25numeric411 unique values
0 missing
V26numeric529 unique values
0 missing
V27numeric539 unique values
0 missing
V28numeric492 unique values
0 missing
V29numeric500 unique values
0 missing
V30numeric535 unique values
0 missing

62 properties

569
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
2
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.
30
Number of numeric attributes.
1
Number of nominal attributes.
3.23
Percentage of binary attributes.
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.54
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
49.21
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
5.99
Third quartile of kurtosis among attributes of the numeric type.
880.58
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
96.77
Percentage of numeric attributes.
17.02
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
3.23
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
0.42
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.98
Third quartile of skewness among attributes of the numeric type.
5.45
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.97
First quartile of kurtosis among attributes of the numeric type.
4.43
Third quartile of standard deviation of attributes of the numeric type.
569.36
Maximum standard deviation of attributes of the numeric type.
37.26
Percentage of instances belonging to the least frequent class.
0.06
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
212
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
7.81
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
0.98
First quartile of skewness among attributes of the numeric type.
61.89
Mean of means among attributes of the numeric type.
0.02
First quartile of standard deviation of attributes of the numeric type.
0.63
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.95
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3.02
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.22
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.74
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
62.74
Percentage of instances belonging to the most frequent class.
34.9
Mean standard deviation of attributes of the numeric type.
1.42
Second quartile (Median) of skewness among attributes of the numeric type.
357
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

16 tasks

140154 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
76354 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1297 runs - target_feature: Class
1296 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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