#' Wrapper to add `tree` engine to the parsnip `decision_tree` model
#' specification
#'
#' @return NULL
#' @export
add_decision_tree_tree <- function() {
object <- NULL
new_data <- NULL
parsnip::set_model_engine("decision_tree", mode = "regression", eng = "tree")
parsnip::set_dependency("decision_tree", eng = "tree", pkg = "tree")
parsnip::set_encoding(
model = "decision_tree",
eng = "tree",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_fit(
model = "decision_tree",
eng = "tree",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "treesnip", fun = "train_tree"),
defaults = list()
)
)
parsnip::set_model_arg(
model = "decision_tree",
eng = "tree",
parsnip = "min_n",
original = "minsize",
func = list(pkg = "dials", fun = "min_n"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "decision_tree",
eng = "tree",
parsnip = "cost_complexity",
original = "mindev",
func = list(pkg = "dials", fun = "cost_complexity"),
has_submodel = FALSE
)
parsnip::set_pred(
model = "decision_tree",
eng = "tree",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data)
)
)
)
}
#' Simple wraper around `tree::tree`
#'
#' Simple wraper aroound `tree::tree` that adds a simple
#' class and sets the `mincut` argument to 0.
#'
#' @param ... arguments passed directly to `[tree::tree()]`.
#'
#' @export
train_tree <- function(...) {
mod <- tree::tree(..., mincut = 0)
class(mod) <- c("treesnip", "tree")
mod
}
#' @export
print.treesnip <- tree:::print.tree
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