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#' Naive Bayes models
#'
#' @description
#'
#' `naive_Bayes()` defines a model that uses Bayes' theorem to compute the
#' probability of each class, given the predictor values. This function can fit
#' classification models.
#'
#' \Sexpr[stage=render,results=rd]{parsnip:::make_engine_list("naive_Bayes")}
#'
#' More information on how \pkg{parsnip} is used for modeling is at
#' \url{https://www.tidymodels.org/}.
#'
#' @inheritParams nearest_neighbor
#' @inheritParams discrim_linear
#' @param smoothness An non-negative number representing the the relative
#' smoothness of the class boundary. Smaller examples result in model flexible
#' boundaries and larger values generate class boundaries that are less
#' adaptable
#' @param Laplace A non-negative value for the Laplace correction to smoothing
#' low-frequency counts.
#'
#' @templateVar modeltype naive_Bayes
#' @template spec-details
#'
#' @template spec-references
#'
#' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("naive_Bayes")}
#' @export
naive_Bayes <-
function(mode = "classification", smoothness = NULL, Laplace = NULL, engine = "klaR") {
args <-
list(
smoothness = rlang::enquo(smoothness),
Laplace = rlang::enquo(Laplace)
)
new_model_spec(
"naive_Bayes",
args = args,
eng_args = NULL,
mode = mode,
user_specified_mode = !missing(mode),
method = NULL,
engine = engine,
user_specified_engine = !missing(engine)
)
}
# ------------------------------------------------------------------------------
#' @method update naive_Bayes
#' @rdname parsnip_update
#' @inheritParams naive_Bayes
#' @export
update.naive_Bayes <-
function(object,
smoothness = NULL, Laplace = NULL,
fresh = FALSE, ...) {
args <-
list(
smoothness = rlang::enquo(smoothness),
Laplace = rlang::enquo(Laplace)
)
update_spec(
object = object,
parameters = NULL,
args_enquo_list = args,
fresh = fresh,
cls = "naive_Bayes",
...
)
}
# ------------------------------------------------------------------------------
set_new_model("naive_Bayes")
set_model_mode("naive_Bayes", "classification")
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