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).
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
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
fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.
Hastie, Tibshirani & Buja (1994) Flexible Discriminant Analysis by Optimal Scoring, Journal of the American Statistical Association, 89:428, 1255-1270
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