For this engine, there is a single mode: classification
This model has 2 tuning parameter:
frac_common_cov
: Fraction of the Common Covariance Matrix (type: double, default: (see below))
frac_identity
: Fraction of the Identity Matrix (type: double, default: (see below))
Some special cases for the RDA model:
frac_identity = 0
and frac_common_cov = 1
is a linear discriminant analysis (LDA) model.
frac_identity = 0
and frac_common_cov = 0
is a quadratic discriminant analysis (QDA) model.
The discrim extension package is required to fit this model.
library(discrim)
discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) %>%
set_engine("klaR") %>%
translate()
## Regularized Discriminant Model Specification (classification)
##
## Main Arguments:
## frac_common_cov = numeric(0)
## frac_identity = numeric(0)
##
## Computational engine: klaR
##
## Model fit template:
## klaR::rda(formula = missing_arg(), data = missing_arg(), lambda = numeric(0),
## gamma = numeric(0))
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 within each outcome class. For this reason, zero-variance predictors (i.e., with a single unique value) within each class should be eliminated before fitting the model.
The underlying model implementation does not allow for case weights.
Friedman, J (1989). Regularized Discriminant Analysis. Journal of the American Statistical Association, 84, 165-175.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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