#' Swiss banknote data (UCI version)
#'
#' Data were extracted from images that were taken from genuine \code{class = 1}
#' and forged \code{class = 0} banknote-like specimens. For digitization, an
#' industrial camera usually used for print inspection was used. The final
#' images contained 400 x 400 pixels. Due to the object lens and distance to the
#' investigated object, gray-scale pictures with a resolution of about 660 dpi
#' were gained. Wavelet transformation tools were used to extract features from
#' the images.
#'
#' @docType data
#'
#' @keywords datasets
#'
#' @format A data frame with 1372 rows and 5 variables.
#'
#' @details
#'
#' \describe{
#'
#' \item{vow}{Variance of the wavelet transformed image (continuous)}
#' \item{sow}{Skewness of the wavelet transformed image (continuous)}
#' \item{kow}{Kurtosis of the wavelet transformed image (continuous)}
#' \item{eoi}{Entropy of the image (continuous)}
#' \item{class}{Integer specifying whether or not the specimen was genuine
#' (\code{class = 1}) or forged (\code{class = 0}).}
#'
#' }
#'
#' @name banknote2
#'
#' @source
#' Dua, D. and Graff, C. (2019). \emph{UCI Machine Learning Repository}
#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
#' School of Information and Computer Science.
NULL
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.