For this engine, there is a single mode: classification
This engine has no tuning parameter arguments in [discrim_linear()].
However, there are a few engine-specific parameters that can be set or optimized when calling [set_engine()]:
lambda
: the shrinkage parameters for the correlation matrix. This maps to the \pkg{dials} parameter [dials::shrinkage_correlation()].
lambda.var
: the shrinkage parameters for the predictor variances. This maps to [dials::shrinkage_variance()].
lambda.freqs
: the shrinkage parameters for the class frequencies. This maps to [dials::shrinkage_frequencies()].
diagonal
: a logical to make the model covariance diagonal or not. This maps to [dials::diagonal_covariance()].
The discrim extension package is required to fit this model.
library(discrim)
discrim_linear() %>%
set_engine("sda") %>%
translate()
## Linear Discriminant Model Specification (classification)
##
## Computational engine: sda
##
## Model fit template:
## sda::sda(Xtrain = missing_arg(), L = missing_arg(), verbose = 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.
The underlying model implementation does not allow for case weights.
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