For this engine, there is a single mode: classification
This model has 1 tuning parameter:
penalty
: Amount of Regularization (type: double, default: 1.0)The discrim extension package is required to fit this model.
library(discrim)
discrim_linear(penalty = numeric(0)) %>%
set_engine("mda") %>%
translate()
## Linear Discriminant Model Specification (classification)
##
## Main Arguments:
## penalty = numeric(0)
##
## Computational engine: mda
##
## Model fit template:
## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
## lambda = numeric(0), method = mda::gen.ridge, keep.fitted = FALSE)
Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
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.
This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on tidymodels.org
.
The fit()
and fit_xy()
arguments have arguments called case_weights
that expect vectors of case weights.
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