R/zoo.R

In diversityForest: Innovative Complex Split Procedures in Random Forests Through Candidate Split Sampling

##' Data on biological species
##'
##' This data set describes 101 different biological species using 16 simple attributes,
##'  where 15 of these are binary and one is metric (the number of legs). The outcome 
##'  "mammal vs. other" (\code{type}) is binary.
##'
##' The variables are as follows:
##' \itemize{
##'   \item \code{hair}. factor. Presence of hairs (true = yes; false = no)
##'   \item \code{feathers}. factor. Presence of feathers (true = yes; false = no)
##'   \item \code{eggs}. factor. Does the species lay eggs? (true = yes; false = no)
##'   \item \code{milk}. factor. Does the species give milk? (true = yes; false = no)  
##'   \item \code{airborne}. factor. Does the species fly? (true = yes; false = no)   
##'   \item \code{aquatic}. factor. Does the species live in the water? (true = yes; false = no) 
##'   \item \code{predator}. factor. Is the species a predator? (true = yes; false = no)
##'   \item \code{toothed}. factor. Presence of teeth (true = yes; false = no)  
##'   \item \code{backbone}. factor. Presence of backbone (true = yes; false = no)
##'   \item \code{breathes}. factor. Does the species breathe with lungs? (true = yes; false = no)
##'   \item \code{venomous}. factor. Is the species venomous? (true = yes; false = no)
##'   \item \code{fins}. factor. Presence of fins (true = yes; false = no)  
##'   \item \code{legs}. metric. Number of legs
##'   \item \code{tail}. factor. Presence of tail (true = yes; false = no)  
##'   \item \code{domestic}. factor. Is the species domestic? (true = yes; false = no)  
##'   \item \code{catsize}. factor. Is the species large? (true = yes; false = no)   
##'   \item \code{type}. factor. Binary outcome variable - type of species ('mammal' vs. 'other')
##' }
##' \verb{ }\cr
##' The original openML dataset contains an additional variable \code{animal}, which is removed in this 
##' version of the data set. This variable provided the names of all species.
##'
##' @format A data frame with 101 observations, 16 covariates and one binary outcome variable
##' @source OpenML: data.name: zoo, data.id: 965, link: \url{https://www.openml.org/d/965/} (Accessed: 29/08/2024)
##'
##' @examples
##' 
##' ##' Load data:
##' data(zoo)
##' 
##' ##' Numbers of observations in the two classes:
##' table(zoo$type)
##' 
##' ##' Dimension of data:
##' dim(zoo)
##'
##' ##' First rows of data:
##' head(zoo) 
##' 
##' @references
##' \itemize{
##'   \item Vanschoren, J., van Rijn, J. N., Bischl, B., Torgo, L. (2013). OpenML: networked science in machine learning. SIGKDD Explorations 15(2):49-60, <\doi{10.1145/2641190.2641198}>.
##'   \item Dua, D., Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. \url{https://archive.ics.uci.edu/ml/}.
##'   }
##'
##' @name zoo
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