R/body_fat.R

In ODRF: Oblique Decision Random Forest for Classification and Regression

#' Body Fat Prediction Dataset
#'
#' Lists estimates of the percentage of body fat determined by underwater
#' weighing and various body circumference measurements for 252 men.
#' Accurate measurement of body fat is inconvenient/costly and it is desirable to have easy methods of estimating body fat that are not inconvenient/costly.
#'
#' The variables listed below, from left to right, are:
#' \itemize{
#' \item Density determined from underwater weighing
#' \item Age (years)
#' \item Weight (lbs)
#' \item Height (inches)
#' \item Neck circumference (cm)
#' \item Chest circumference (cm)
#' \item Abdomen 2 circumference (cm)
#' \item Hip circumference (cm)
#' \item Thigh circumference (cm)
#' \item Knee circumference (cm)
#' \item Ankle circumference (cm)
#' \item Biceps (extended) circumference (cm)
#' \item Forearm circumference (cm)
#' \item Wrist circumference (cm)
#' }
#'
#' @docType data
#' @keywords datasets internal
#' @format A data frame with 252 rows and 15 covariate variables and 1 response variable
#' @source \url{https://www.kaggle.com/datasets/fedesoriano/body-fat-prediction-dataset}
#' @references Bailey, Covert (1994). Smart Exercise: Burning Fat, Getting Fit, Houghton-Mifflin Co., Boston, pp. 179-186.
#' @name body_fat
#'
#' @seealso  \code{\link{breast_cancer}} \code{\link{seeds}}
#' @examples
#' data(body_fat)
#' set.seed(221212)
#' train <- sample(1:252, 60)
#' train_data <- data.frame(body_fat[train, ])
#' test_data <- data.frame(body_fat[-train, ])
#'\donttest{
#' forest <- ODRF(Density ~ ., train_data, split = "mse", parallel = FALSE, ntrees = 50)
#' pred <- predict(forest, test_data[, -1])
#' # estimation error
#' mean((pred - test_data[, 1])^2)
#'}
#' tree <- ODT(Density ~ ., train_data, split = "mse")
#' pred <- predict(tree, test_data[, -1])
#' # estimation error
#' mean((pred - test_data[, 1])^2)
#'
NULL