Nothing
set_new_model("svm_linear")
set_model_mode("svm_linear", "classification")
set_model_mode("svm_linear", "regression")
# ------------------------------------------------------------------------------
set_model_engine("svm_linear", "classification", "LiblineaR")
set_model_engine("svm_linear", "regression", "LiblineaR")
set_dependency("svm_linear", "LiblineaR", "LiblineaR")
set_model_arg(
model = "svm_linear",
eng = "LiblineaR",
parsnip = "cost",
original = "C",
func = list(pkg = "dials", fun = "cost", range = c(-10, 5)),
has_submodel = FALSE
)
set_model_arg(
model = "svm_linear",
eng = "LiblineaR",
parsnip = "margin",
original = "svr_eps",
func = list(pkg = "dials", fun = "svm_margin"),
has_submodel = FALSE
)
set_fit(
model = "svm_linear",
eng = "LiblineaR",
mode = "regression",
value = list(
interface = "matrix",
protect = c("x", "y"),
data = c(x = "data", y = "target"),
func = c(pkg = "LiblineaR", fun = "LiblineaR"),
defaults = list(type = 11)
)
)
set_fit(
model = "svm_linear",
eng = "LiblineaR",
mode = "classification",
value = list(
interface = "matrix",
data = c(x = "data", y = "target"),
protect = c("x", "y"),
data = c(x = "data", y = "target"),
func = c(pkg = "LiblineaR", fun = "LiblineaR"),
defaults = list(type = 1)
)
)
set_encoding(
model = "svm_linear",
eng = "LiblineaR",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = TRUE
)
)
set_encoding(
model = "svm_linear",
eng = "LiblineaR",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = TRUE
)
)
set_pred(
model = "svm_linear",
eng = "LiblineaR",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = svm_linear_post,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newx = quote(new_data)
)
)
)
set_pred(
model = "svm_linear",
eng = "LiblineaR",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = quote(object$fit),
newx = quote(new_data))
)
)
set_pred(
model = "svm_linear",
eng = "LiblineaR",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = svm_linear_post,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newx = expr(as.matrix(new_data))
)
)
)
set_pred(
model = "svm_linear",
eng = "LiblineaR",
mode = "classification",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = quote(object$fit),
newx = quote(new_data))
)
)
# ------------------------------------------------------------------------------
set_model_engine("svm_linear", "classification", "kernlab")
set_model_engine("svm_linear", "regression", "kernlab")
set_dependency("svm_linear", "kernlab", "kernlab")
set_model_arg(
model = "svm_linear",
eng = "kernlab",
parsnip = "cost",
original = "C",
func = list(pkg = "dials", fun = "cost", range = c(-10, 5)),
has_submodel = FALSE
)
set_model_arg(
model = "svm_linear",
eng = "kernlab",
parsnip = "margin",
original = "epsilon",
func = list(pkg = "dials", fun = "svm_margin"),
has_submodel = FALSE
)
set_fit(
model = "svm_linear",
eng = "kernlab",
mode = "regression",
value = list(
interface = "formula",
data = c(formula = "x", data = "data"),
protect = c("x", "data"),
func = c(pkg = "kernlab", fun = "ksvm"),
defaults = list(kernel = "vanilladot")
)
)
set_fit(
model = "svm_linear",
eng = "kernlab",
mode = "classification",
value = list(
interface = "formula",
data = c(formula = "x", data = "data"),
protect = c("x", "data"),
func = c(pkg = "kernlab", fun = "ksvm"),
defaults = list(kernel = "vanilladot")
)
)
set_encoding(
model = "svm_linear",
eng = "kernlab",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "svm_linear",
eng = "kernlab",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = svm_reg_linear_post,
func = c(pkg = "kernlab", fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "response"
)
)
)
set_pred(
model = "svm_linear",
eng = "kernlab",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "kernlab", fun = "predict"),
args = list(object = quote(object$fit), newdata = quote(new_data))
)
)
set_encoding(
model = "svm_linear",
eng = "kernlab",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "svm_linear",
eng = "kernlab",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "kernlab", fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "response"
)
)
)
set_pred(
model = "svm_linear",
eng = "kernlab",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = function(result, object) as_tibble(result),
func = c(pkg = "kernlab", fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "probabilities"
)
)
)
set_pred(
model = "svm_linear",
eng = "kernlab",
mode = "classification",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "kernlab", fun = "predict"),
args = list(object = quote(object$fit), newdata = quote(new_data))
)
)
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