#' @title Regression Random Forest Learner
#' @author pat-s
#' @name mlr_learners_regr.randomForest
#'
#' @description
#' Random forest for regression.
#' Calls [randomForest::randomForest()] from \CRANpkg{randomForest}.
#'
#' @template learner
#' @templateVar id regr.randomForest
#'
#' @references
#' `r format_bib("breiman_2001")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerRegrRandomForest = R6Class("LearnerRegrRandomForest",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
ntree = p_int(default = 500L, lower = 1L,
tags = "train"),
mtry = p_int(lower = 1L, tags = "train"),
replace = p_lgl(default = TRUE, tags = "train"),
strata = p_uty(tags = "train"),
sampsize = p_uty(tags = "train"),
nodesize = p_int(default = 5L, lower = 1L,
tags = "train"),
maxnodes = p_int(lower = 1L, tags = "train"),
importance = p_fct(default = FALSE,
levels = c("mse", "nudepurity", "none"),
special_vals = list(FALSE),
tags = "train"),
localImp = p_lgl(default = FALSE, tags = "train"),
proximity = p_lgl(default = FALSE, tags = c("train", "predict")),
oob.prox = p_lgl(tags = "train"),
norm.votes = p_lgl(default = TRUE, tags = "train"),
do.trace = p_lgl(default = FALSE, tags = "train"),
keep.forest = p_lgl(default = TRUE, tags = "train"),
keep.inbag = p_lgl(default = FALSE, tags = "train"),
predict.all = p_lgl(default = FALSE, tags = "predict"),
nodes = p_lgl(default = FALSE, tags = "predict")
)
super$initialize(
id = "regr.randomForest",
packages = c("mlr3extralearners", "randomForest"),
feature_types = c("integer", "numeric", "factor", "ordered", "logical"),
predict_types = "response",
param_set = ps,
properties = c("weights", "importance", "oob_error"),
man = "mlr3extralearners::mlr_learners_regr.randomForest",
label = "Random Forest"
)
},
#' @description
#' The importance scores are extracted from the slot `importance`.
#' Parameter 'importance' must be set to either `"mse"` or `"nodepurity"`.
#' @return Named `numeric()`.
importance = function() {
if (is.null(self$model)) {
stopf("No model stored")
}
imp = data.frame(self$model$importance)
colnames(imp)[colnames(imp) == "X.IncMSE"] = "%IncMSE"
## correct for language error on special characters
pars = self$param_set$get_values()
scores = switch(pars[["importance"]],
"mse" = imp[["%IncMSE"]],
"nodepurity" = imp[["IncNodePurity"]],
stop("No importance available. Try setting 'importance' to 'mse' or 'nodepurity'.")
)
sort(stats::setNames(scores, rownames(imp)), decreasing = TRUE)
},
#' @description
#' OOB errors are extracted from the model slot `mse`.
#' @return `numeric(1)`.
oob_error = function() {
mean(self$model$mse)
}
),
private = list(
.train = function(task) {
pars = self$param_set$get_values(tags = "train")
formula = task$formula()
data = task$data()
# randomForest() only accepts TRUE and FALSE during fitting and the
# specific importance methods are honored post-fitting only
if (!is.null(pars$importance)) {
pars$importance = TRUE
}
invoke(randomForest::randomForest,
formula = formula,
data = data, .args = pars)
},
.predict = function(task) {
pars = self$param_set$get_values(tags = "predict")
newdata = ordered_features(task, self)
type = self$predict_type
pred = invoke(predict, self$model,
newdata = newdata,
type = type, .args = pars)
list(response = pred)
}
)
)
.extralrns_dict$add("regr.randomForest", LearnerRegrRandomForest)
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