R/SplitSoftening.R

In SplitSoftening: Softening Splits in Decision Trees

#' Package: Softening splits in classification trees
#'
#' The basic idea of split softening is to modify the process of classification
#' of an input case with a decision tree such that
#' in the area near the threshold of a softened split both branches
#' of the tree are used to provide a prediction for the submitted case
#' and their results are combined.
#'
#' Functions in this package allow to add softening to the nodes of
#' a classification tree created with the package \code{tree}.
#' Each node where a decision on a continuous variable is made is enriched
#' with softening parameters which specify the boundaries of the softening area
#' and which together with the original split threshold determine the weights
#' of the branches when combined.
#'
#' The weights of branches are (1/2, 1/2) in the original split threshold.
#' Other points inside the softening area have weights given by linear interpolation
#' to reach the values (0, 1), or vice versa, on the boundaries of the softening area.
#'
#' A data structure for a decision tree prepared for softening
#' can be created from a \code{tree} object
#' with the \code{\link{softsplits}} function.
#'
#' Softening parameters may be set to the `soft tree' structure.
#' The package offers the following functions for this purpose:
#' \itemize{
#' 	\item\code{\link{softening.by.data.range}}
#' 	\item\code{\link{softening.by.esd}}
#' 	\item\code{\link{softening.optimized}}
#' 	\item\code{\link{soften}}
#' }
#'
#' A softened tree might be used to obtain a prediction for a dataset
#' using the \code{\link{predictSoftsplits}} function.
#'
#' @references
#' \enc{Dvořák}{Dvorak}, J. (2019), \emph{Classification trees with soft splits optimized for ranking} <doi:10.1007/s00180-019-00867-1>
#' \code{https://rdcu.be/bkeW2}
#'
#' @docType package
#' @name SplitSoftening
#' @useDynLib SplitSoftening
NULL