\name{BreastCancer}
\title{Wisconsin Breast Cancer Database}
\usage{
data("BreastCancer", package = "mlbench")
}
\alias{BreastCancer}
\format{A data frame with 699 observations on 11 variables, one being a
    character variable, 9 being ordered or nominal, and 1 target class.

    \tabular{cll}{ 
 [,1] \tab Id \tab Sample code number\cr
 [,2] \tab Cl.thickness \tab Clump Thickness\cr
 [,3] \tab Cell.size \tab Uniformity of Cell Size\cr
 [,4] \tab Cell.shape \tab Uniformity of Cell Shape\cr
 [,5] \tab Marg.adhesion  \tab Marginal Adhesion\cr
 [,6] \tab Epith.c.size \tab Single Epithelial Cell Size\cr
 [,7] \tab Bare.nuclei \tab Bare Nuclei\cr
 [,8] \tab Bl.cromatin \tab Bland Chromatin\cr
 [,9] \tab Normal.nucleoli \tab Normal Nucleoli\cr
[,10] \tab Mitoses \tab Mitoses\cr
[,11] \tab Class \tab Class
}
    }
\description{
    The objective is to identify each of a number of benign or malignant
    classes. Samples arrive periodically as
    Dr. Wolberg reports his clinical cases.
    The database therefore reflects this chronological grouping of the
    data.  This grouping information appears immediately below, having been
    removed from the data itself.  Each variable except for the first was
    converted into 11 primitive numerical attributes with values ranging
    from 0 through 10.  There are 16 missing attribute values. See cited
    below for more details.}
\source{
    \itemize{
       	\item Creator: Dr. WIlliam H. Wolberg (physician); University of
	Wisconsin Hospital ;Madison; Wisconsin; USA 
        \item Donor: Olvi Mangasarian (mangasarian@cs.wisc.edu)
	\item Received: David W. Aha (aha@cs.jhu.edu)
      }

    These data have been taken from the UCI Repository Of Machine Learning
    Databases \bibcitep{Blake+Merz:1998} and were converted to R format by
    Evgenia Dimitriadou in the late 1990s.

    The current version of the UC Irvine Machine Learning Repository
    Breast Cancer Wisconsin (Original) data set is available from
    \doi{10.24432/C5HP4Z}.
}
\references{   
   1. \bibshow{Wolberg+Mangasarian:1990}\cr
      - Size of data set: only 369 instances (at that point in time)\cr
      - Collected classification results: 1 trial only\cr
      - Two pairs of parallel hyperplanes were found to be consistent with
         50\% of the data\cr
         - Accuracy on remaining 50\% of dataset: 93.5\%\cr
      - Three pairs of parallel hyperplanes were found to be consistent with
         67\% of data\cr
         - Accuracy on remaining 33\% of dataset: 95.9\%

   2. \bibshow{Zhang:1992}\cr
      - Size of data set: only 369 instances (at that point in time)\cr
      - Applied 4 instance-based learning algorithms\cr
      - Collected classification results averaged over 10 trials\cr
      - Best accuracy result: \cr
         - 1-nearest neighbor: 93.7\%\cr
         - trained on 200 instances, tested on the other 169\cr
      - Also of interest:\cr
         - Using only typical instances: 92.2\% (storing only 23.1 instances)\cr
         - trained on 200 instances, tested on the other 169


  \bibshow{Blake+Merz:1998}
}
\keyword{datasets}
\examples{
data("BreastCancer", package = "mlbench")
summary(BreastCancer)
}
    
