#' Boosting with Classification Trees
#'
#' Fits the AdaBoost.M1 (Freund and Schapire, 1996) and SAMME (Zhu et al., 2009)
#' algorithms using classification trees as single classifiers.
#'
#' @param boos if \code{TRUE}, then bootstrap samples are drawn from the
#' training set using the observation weights at each iteration. If
#' \code{FALSE}, then all observations are used with their weights.
#' @param mfinal number of iterations for which boosting is run.
#' @param coeflearn learning algorithm.
#' @param minsplit minimum number of observations that must exist in a node in
#' order for a split to be attempted.
#' @param minbucket minimum number of observations in any terminal node.
#' @param cp complexity parameter.
#' @param maxcompete number of competitor splits retained in the output.
#' @param maxsurrogate number of surrogate splits retained in the output.
#' @param usesurrogate how to use surrogates in the splitting process.
#' @param xval number of cross-validations.
#' @param surrogatestyle controls the selection of a best surrogate.
#' @param maxdepth maximum depth of any node of the final tree, with the root
#' node counted as depth 0.
#'
#' @details
#' \describe{
#' \item{Response types:}{\code{factor}}
#' \item{\link[=TunedModel]{Automatic tuning} of grid parameters:}{
#' \code{mfinal}, \code{maxdepth}, \code{coeflearn}*
#' }
#' }
#' * excluded from grids by default
#'
#' Further model details can be found in the source link below.
#'
#' @return \code{MLModel} class object.
#'
#' @seealso \code{\link[adabag]{boosting}}, \code{\link{fit}},
#' \code{\link{resample}}
#'
#' @examples
#' \donttest{
#' ## Requires prior installation of suggested package adabag to run
#'
#' fit(Species ~ ., data = iris, model = AdaBoostModel(mfinal = 5))
#' }
#'
AdaBoostModel <- function(
boos = TRUE, mfinal = 100, coeflearn = c("Breiman", "Freund", "Zhu"),
minsplit = 20, minbucket = round(minsplit/3), cp = 0.01, maxcompete = 4,
maxsurrogate = 5, usesurrogate = 2, xval = 10, surrogatestyle = 0,
maxdepth = 30
) {
coeflearn <- match.arg(coeflearn)
MLModel(
name = "AdaBoostModel",
label = "Boosting with Classification Trees",
packages = "adabag",
response_types = "factor",
predictor_encoding = "model.frame",
na.rm = "response",
params = new_params(environment()),
gridinfo = new_gridinfo(
param = c("mfinal", "maxdepth", "coeflearn"),
get_values = c(
function(n, ...) round_int(seq_range(0, 25, c(1, 200), n + 1)),
function(n, ...) seq_len(min(n, 30)),
function(n, ...) head(c("Breiman", "Freund", "Zhu"), n)
),
default = c(TRUE, TRUE, FALSE)
),
fit = function(formula, data, weights, boos, mfinal, coeflearn, ...) {
adabag::boosting(
formula, data = as.data.frame(formula, data = data), boos = boos,
mfinal = mfinal, coeflearn = coeflearn, control = list(...)
)
},
predict = function(object, newdata, ...) {
newdata <- as.data.frame(newdata)
predict(object, newdata = newdata)$prob
},
varimp = function(object, ...) {
structure(object$importance, metric = "gini")
}
)
}
MLModelFunction(AdaBoostModel) <- NULL
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