For this engine, there is a single mode: classification
This model has 2 tuning parameter:
smoothness
: Kernel Smoothness (type: double, default: 1.0)
Laplace
: Laplace Correction (type: double, default: 0.0)
Note that the engine argument usekernel
is set to TRUE
by default when using the klaR
engine.
The discrim extension package is required to fit this model.
library(discrim)
naive_Bayes(smoothness = numeric(0), Laplace = numeric(0)) %>%
set_engine("klaR") %>%
translate()
## Naive Bayes Model Specification (classification)
##
## Main Arguments:
## smoothness = numeric(0)
## Laplace = numeric(0)
##
## Computational engine: klaR
##
## Model fit template:
## discrim::klar_bayes_wrapper(x = missing_arg(), y = missing_arg(),
## adjust = numeric(0), fL = numeric(0), usekernel = TRUE)
The columns for qualitative predictors should always be represented as factors (as opposed to dummy/indicator variables). When the predictors are factors, the underlying code treats them as multinomial data and appropriately computes their conditional distributions.
Variance calculations are used in these computations so zero-variance predictors (i.e., with a single unique value) should be eliminated before fitting the model.
The underlying model implementation does not allow for case weights.
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