Nothing
#' @docType data
#' @keywords datasets
#' @name corral_augmented
#' @usage data(corral_augmented)
#' @title Augmented corral data set: synthetic data with correlated attributes augmented with noise features
#' @description This is an artificial domain where the target concept is (X1^X2) V (X3^X4). \cr
#' Data set from John et al (1994). Training and test splits from SGI. \cr
#' The first 6 features are the real features from the original corral data set.
#' The rest are noise features added by V. Krakovna by shuffling copies of real features.\cr
#' The SBFC paper uses subsets of this data set with the first 100 and 1000 features.
#' @format \describe{
#' \item{\code{TrainX}}{A matrix with 128 rows and 10000 columns.}
#' \item{\code{TrainY}}{A vector with 128 rows.}
#' }
#' @examples corral_result = sbfc(data=list(TrainX=corral_augmented$TrainX[,1:6],
#' TrainY = corral_augmented$TrainY))
#' corral100_result = sbfc(data=list(TrainX=corral_augmented$TrainX[,1:100],
#' TrainY = corral_augmented$TrainY))
#' @references \href{https://ai.stanford.edu/~ronnyk/ml94.pdf}{John et al (1994) paper introducing the corral data set}
#' @references \href{https://arxiv.org/abs/1506.02371}{SBFC paper describing augmentation of corral data set}
NULL
#' @docType data
#' @keywords datasets
#' @name heart
#' @usage data(heart)
#' @title Heart disease data set: disease outcomes given health attributes
#' @description Data set from UCI repository, discretized using the \code{mdlp} package.
#' @format \describe{
#' \item{\code{TrainX}}{A matrix with 270 rows and 13 columns.}
#' \item{\code{TrainY}}{A vector with 270 rows.}
#' }
#' @references \href{https://archive.ics.uci.edu/ml/datasets/Statlog+(Heart)}{UCI heart data set}
NULL
#' @docType data
#' @keywords datasets
#' @name madelon
#' @usage data(madelon)
#' @title Madelon data set: synthetic data from NIPS 2003 feature selection challenge
#' @description This is a two-class classification problem.
#' The difficulty is that the problem is multivariate and highly non-linear.
#' Of the 500 features, 20 are real features, 480 are noise features. \cr
#' Data set from UCI repository, discretized using median cutoffs.
#' @format \describe{
#' \item{\code{TrainX}}{A matrix with 2000 rows and 500 columns.}
#' \item{\code{TrainY}}{A vector with 2000 rows.}
#' \item{\code{TestX}}{A matrix with 600 rows and 500 columns.}
#' \item{\code{TestY}}{A vector with 600 rows.}
#' }
#' @references \href{https://archive.ics.uci.edu/ml/datasets/Madelon}{UCI madelon data set}
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.