Linear discriminant analysis via flexible discriminant analysis
Source:R/discrim_linear_mda.R
details_discrim_linear_mda.Rdmda::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.