R/seeds.R

In ODRF: Oblique Decision Random Forest for Classification and Regression

#' seeds Data Set
#'
#' Measurements of geometrical properties of kernels belonging to three different varieties of wheat.
#' A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes.
#'
#' The variables listed below, from left to right, are:
#' \itemize{
#' \item area A
#' \item perimeter P
#' \item compactness C = 4*pi*A/P^2
#' \item length of kernel
#' \item width of kernel
#' \item asymmetry coefficient
#' \item length of kernel groove
#' \item varieties of wheat (1, 2, 3 for Kama, Rosa and Canadian respectively)
#' }
#'
#' @docType data
#' @keywords datasets internal
#' @format A data frame with 209 rows and 7 covariate variables and 1 response variable.
#' @source \url{https://archive.ics.uci.edu/ml/datasets/seeds}
#' @references M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, 'A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images', in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 15-24.
#' @name seeds
#'
#' @seealso  \code{\link{body_fat}} \code{\link{breast_cancer}}
#' @examples
#' data(seeds)
#' set.seed(221212)
#' train <- sample(1:209, 80)
#' train_data <- data.frame(seeds[train, ])
#' test_data <- data.frame(seeds[-train, ])
#'
#' forest <- ODRF(varieties_of_wheat ~ ., train_data,
#'   split = "gini", parallel = FALSE, ntrees = 50
#' )
#' pred <- predict(forest, test_data[, -8])
#' # classification error
#' (mean(pred != test_data[, 8]))
#'
#' tree <- ODT(varieties_of_wheat ~ ., train_data, split = "gini")
#' pred <- predict(tree, test_data[, -8])
#' # classification error
#' (mean(pred != test_data[, 8]))
NULL