Nothing
make_c5 <- function() {
parsnip::set_model_engine("C5_rules", "classification", "C5.0")
parsnip::set_dependency("C5_rules", "C5.0", "C50", "classification")
parsnip::set_dependency("C5_rules", "C5.0", "rules", "classification")
parsnip::set_fit(
model = "C5_rules",
eng = "C5.0",
mode = "classification",
value = list(
interface = "data.frame",
protect = c("x", "y", "weights"),
func = c(pkg = "rules", fun = "c5_fit"),
defaults = list()
)
)
parsnip::set_encoding(
model = "C5_rules",
eng = "C5.0",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_model_arg(
model = "C5_rules",
eng = "C5.0",
parsnip = "trees",
original = "trials",
func = list(pkg = "dials", fun = "trees"),
has_submodel = TRUE
)
parsnip::set_model_arg(
model = "C5_rules",
eng = "C5.0",
parsnip = "min_n",
original = "minCases",
func = list(pkg = "dials", fun = "min_n"),
has_submodel = FALSE
)
parsnip::set_pred(
model = "C5_rules",
eng = "C5.0",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data),
type = "class"
)
)
)
parsnip::set_pred(
model = "C5_rules",
eng = "C5.0",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = prob_matrix_to_tibble,
func = c(fun = "predict"),
args =
list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data),
type = "prob"
)
)
)
}
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