Linear discriminant analysis via flexible discriminant analysis
Source:R/discrim_linear_mda.R
details_discrim_linear_mda.Rd
mda::fda()
(in conjunction with mda::gen.ridge()
can fit a linear
discriminant analysis model that penalizes the predictor coefficients with a
quadratic penalty (i.e., a ridge or weight decay approach).
Details
For this engine, there is a single mode: classification
Tuning Parameters
This model has 1 tuning parameter:
penalty
: Amount of Regularization (type: double, default: 1.0)
Translation from parsnip to the original package
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)
Preprocessing requirements
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 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.
Case weights
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.