# These functions define the discriminant analysis models. They are executed when
# this package is loaded via `.onLoad()` and modify the parsnip package's
# model environment.
# These functions are tested indirectly when the models are used. Since this
# function is executed on package startup, you can't execute them to test since
# they are already in the parsnip model database. We'll exclude them from
# coverage stats for this reason.
# nocov
make_naive_Bayes_naivebayes <- function() {
parsnip::set_model_engine("naive_Bayes", "classification", "naivebayes")
parsnip::set_dependency("naive_Bayes", "naivebayes", "naivebayes")
parsnip::set_dependency("naive_Bayes", "naivebayes", "discrim")
parsnip::set_model_arg(
model = "naive_Bayes",
eng = "naivebayes",
parsnip = "smoothness",
original = "adjust",
func = list(pkg = "dials", fun = "smoothness"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "naive_Bayes",
eng = "naivebayes",
parsnip = "Laplace",
original = "laplace",
func = list(pkg = "dials", fun = "Laplace"),
has_submodel = FALSE
)
parsnip::set_fit(
model = "naive_Bayes",
eng = "naivebayes",
mode = "classification",
value = list(
interface = "data.frame",
protect = c("x", "y"),
func = c(pkg = "naivebayes", fun = "naive_bayes"),
defaults = list(usekernel = TRUE)
)
)
parsnip::set_encoding(
model = "naive_Bayes",
eng = "naivebayes",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "naive_Bayes",
eng = "naivebayes",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
parsnip::set_pred(
model = "naive_Bayes",
eng = "naivebayes",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = prob_matrix_to_tibble,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "prob"
)
)
)
}
make_naive_Bayes_klaR <- function() {
parsnip::set_model_engine("naive_Bayes", "classification", "klaR")
parsnip::set_dependency("naive_Bayes", "klaR", "klaR")
parsnip::set_dependency("naive_Bayes", "klaR", "discrim")
parsnip::set_model_arg(
model = "naive_Bayes",
eng = "klaR",
parsnip = "smoothness",
original = "adjust",
func = list(pkg = "dials", fun = "smoothness"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "naive_Bayes",
eng = "klaR",
parsnip = "Laplace",
original = "fL",
func = list(pkg = "dials", fun = "Laplace"),
has_submodel = FALSE
)
parsnip::set_fit(
model = "naive_Bayes",
eng = "klaR",
mode = "classification",
value = list(
interface = "data.frame",
protect = c("x", "y"),
func = c(pkg = "discrim", fun = "klar_bayes_wrapper"),
defaults = list(usekernel = TRUE)
)
)
parsnip::set_encoding(
model = "naive_Bayes",
eng = "klaR",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "naive_Bayes",
eng = "klaR",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = get_class,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
parsnip::set_pred(
model = "naive_Bayes",
eng = "klaR",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = post_to_tibble,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
parsnip::set_pred(
model = "naive_Bayes",
eng = "klaR",
mode = "classification",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
}
# nocov end
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.