R/carEvaluation.R

In imptree: Classification Trees with Imprecise Probabilities

# Copyright (C) 2012 - 2018  Paul Fink
#
# This file is part of imptree.
#
# imptree is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# imptree is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with imptree.  If not, see <https://www.gnu.org/licenses/>.

#' @docType data
#' @name carEvaluation
#' 
#' @title Car Evaluation Database
#' 
#' @description
#' This data.frame contains the 'Car Evaluation' data set from 
#' the UCI Machine Learning Repository.
#' \cr
#' The 'Car Evaluation data' set gives the acceptance 
#' of a car directly related to the six input attributes:
#' buying, maint, doors, persons, lug_boot, safety.
#' 
#' @usage data(carEvaluation)
#' 
#' @format
#' A data frame with 1728 observations on the following 7 variables,
#' where each row contains information on one car.
#' All variables are factor variables.
#' \describe{
#'   \item{\code{buying}}{Buying price of the car
#'         (Levels: \code{high}, \code{low}, \code{med} ,\code{vhigh})}
#'   \item{\code{maint}}{Price of the maintenance
#'         (Levels: \code{high}, \code{low}, \code{med}, \code{vhigh})}
#'   \item{\code{doors}}{Number of doors
#'         (Levels: \code{2}, \code{3}, \code{4}, \code{5more})}
#'   \item{\code{persons}}{Capacity in terms of persons to carry
#'         (Levels: \code{2}, \code{4}, \code{more})}
#'   \item{\code{lug_boot}}{Size of luggage boot
#'         (Levels: \code{big}, \code{med}, \code{small})}
#'   \item{\code{safety}}{Estimated safety of the car
#'         (Levels: \code{high}, \code{low}, \code{med})}
#'   \item{\code{acceptance}}{Acceptance of the car (target variable)
#'         (Levels: \code{acc}, \code{good}, \code{unacc}, \code{vgood})}
#' }
#' 
#' @details 
#' Car Evaluation Database was derived from a simple hierarchical
#' decision model originally developed for the demonstration of DEX. 
#'
#'  The model evaluates cars according to the following concept structure:
#'  \tabular{ll}{
#'  CAR                 \tab car acceptability\cr
#'  . PRICE             \tab overall price\cr
#'  . . buying          \tab buying price\cr
#'  . . maint           \tab price of the maintenance\cr
#'  . TECH              \tab technical characteristics\cr
#'  . . COMFORT         \tab comfort\cr
#'  . . . doors         \tab number of doors\cr
#'  . . . persons       \tab capacity in terms of persons to carry\cr
#'  . . . lug_boot      \tab the size of luggage boot\cr
#'  . . safety          \tab estimated safety of the car
#'  }
#'
#' Input attributes are printed in lowercase. Besides the target
#' concept (CAR), the model includes three intermediate concepts:
#' PRICE, TECH, COMFORT. 
#'
#' The Car Evaluation Database contains examples with the structural 
#' information removed, i.e., directly relates CAR to the six input 
#' attributes: buying, maint, doors, persons, lug_boot, safety.
#' 
#' @source
#' The original data were taken from the UCI Machine Learning repository 
#' (\url{https://archive.ics.uci.edu/ml/datasets/Car+Evaluation}) and were 
#' converted into R format by Paul Fink.
#'
#' @references M. Bohanec and V. Rajkovic (1988), Knowledge acquisition and explanation for 
#' multi-attribute decision making, \emph{8th Intl. Workshop on Expert 
#' Systems and their Applications}, Avignon, France, 59--78.
#' 
#' @references D. Dua and E. Karra Taniskidou (2017), UCI Machine Learning Repository 
#' \url{http://archive.ics.uci.edu/ml}. Irvine, CA: University of California, 
#' School of Information and Computer Science.
#'
#' @examples
#' data("carEvaluation")
#' summary(carEvaluation)
#' 
#' @keywords datasets
NULL